Prepare for and practice interview questions from Harnham.

Harnham Interview Questions

data and analytics recruitment is our core business and we’re proud to say, our customers believe we’re good at it. in our most recent customer satisfaction survey, 95% of respondents said that they would recommend harnham. harnham has actively chosen to focus on data and analytics, we’ve immersed ourselves in this market and are now an integral part of this business community. our capability has grown to provide recruitment services and advice across the marketing analytics, credit risk, data science, data and technology and digital sectors.

data and analytics recruitment is our core business and we’re proud to say, our customers believe we’re good at it. in our most recent customer satisfaction survey, 95% of respondents said that they would recommend harnham. harnham has actively chosen to focus on data and analytics, we’ve immersed ourselves in this market and are now an integral part of this business community. our capability has grown to provide recruitment services and advice across the marketing analytics, credit risk, data science, data and technology and digital sectors.

Overview

Harnham Interview Guides

Machine Learning

Encoding Categorical Features

Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

Perfectly Separable

Missing Housing Data

Bank Fraud Model

One Million Rides

Data Structures & Algorithms

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Find the missing integer from a array of consequtive integers

Find the Missing Number

Get Top N Frequent Words

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

Move Zeros Back

Write a query to select the top 3 departments with at least ten employees and rank them according to the percentage of their employees making over 100K in salary.

Employee Salaries

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Calculate the first touch attribution for each `user_id` that converted.

First Touch Attribution

Closest SAT Scores

Get the top 3 highest employee salaries by department

Top Three Salaries

Probability

First to Six

500 Cards

Raining in Seattle

Found Item

Given three uniform(0,4) random variables, what is the probability that the median of them is greater than 3?

Median Probability

Statistics

P-value to a Layman

What are the assumptions of linear regression?

Assumptions of Linear Regression

Using R Squared

What statistical test could you use to determine which of two parcel types is better to use, given how often they are damaged?

Different Parcel Effectiveness

Unbiased Estimator

Challenge

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

Brainteasers

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

Describe a data project you worked on. What were some of the challenges you faced?

Describing a data project and its challenges

Hurdles In Data Projects

Analytics

How would you explain what a p-value is to someone who is not technical?

What does a p-value in a statistical test represent?

What does a p-value in a statistical test represent?

P-value to a Layman I

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

P-value to a Layman II

What are the assumptions of linear regression?

Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

Bagging vs Boosting

Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

Strategically resolving misaligned expectations with stakeholders for a successful project outcome

Stakeholder Communication

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

Xgboost vs Random Forest

How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?

What happens when you average the output on multiple decision trees?

What happens when you average the output on multiple decision trees?

Average Trees

Random Forest Explanation

What is the difference between Logistic and Linear Regression?

When would use one instead of the other in practice?

What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

Let’s say that you work at a B2B SAAS company that’s interested in testing the pricing of different levels of subscriptions.

Your project manager comes to you and asks you to run a two-week-long A/B test to test an increase in pricing.

How would you approach designing this test? How would you determine whether the increase in pricing is a good business decision?

Testing Price Increase

A/B Testing

Tell me about a project in which you had to clean and organize a large dataset.

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Data Pipelines

What are the key differences between classification models and regression models?

What is the MOST important difference between regression and classification models?

What is the MOST important difference between regression and classification models?

Classification vs Regression

Classification and Regression

Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.

Note: Can you do it on $O(n)$ time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</code></pre>

Given an array and a target integer, write a function that returns the indices of two integers in the array that add up to the target integer.

Target Indices

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Experiment Validity

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

How would we build this model?

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Let’s say you work at Allstate. Allstate is running <code>N</code> online ads right now. The table <code>ads</code> contains all those ads, ranked by popularity via the <code>id</code> column (e.g., the entry with <code>id = 1</code> is the most popular, etc.).

Create a subquery or common table expression named <code>top_ads</code> containing the top 3 ads (by popularity) and return the number of rows that would result from the following operations

<ol>
<li><code>ads INNER JOIN top_ads</code></li>
<li><code>ads LEFT JOIN top_ads</code></li>
<li><code>ads RIGHT JOIN top_ads</code></li>
<li><code>ads CROSS JOIN top_ads</code></li>
</ol>

Note: Please make the <code>join_type</code> column in your output have the values <code>inner_join</code>, <code>left_join</code>, etc. for each of their respective join types

Note: Please return only one query with each number in a different row

Example:

Input:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>join_type</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>number_of_rows</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Size of Joins

Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

How would you convey insights and the methods you use to a non-technical audience?

Making data-driven insights actionable for those without technical expertise

Simple Explanations

What’s the relationship between PCA and K-means clustering?

What does the variable “k” in k-means clustering refer to?

What does the variable "k" in k-means clustering refer to?

Input of K-means

PCA and K-Means

Let’s say you are working on Google Docs. A product manager comes to you and asks how the product is doing. 

What are the top five metrics that you would start tracking to understand the health of Google Docs?

Docs Metrics

Product Sense & Metrics

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>n = 5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

Let’s say you’re a data engineer at Fidelity Investments, and you’re running a SQL query on a cloud-based data warehouse. All cluster resources and network health metrics look normal, but the query is still taking over 10 minutes to complete.

How would you go about diagnosing and improving the performance of this query?

How would you diagnose and speed up a slow SQL query when system metrics look healthy?

Slow SQL Query

Query Optimization

Given the <code>employees</code> and <code>departments</code> table, write a query to get the top 3 highest employee salaries by department. If the department contains less that 3 employees, the top 2 or the top 1 highest salaries should be listed (assume that each department has at least 1 employee). 

Note: The output should include the full name of the employee in one column, the department name, and the salary. The output should be sorted by department name in ascending order and salary in descending order. 

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>employee_nam</code>e</td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>department_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

How would you explain the bias variance tradeoff in machine learning to a high school student?

Replaced by QUESTION 791 - Should be deleted

Bias Variance Tradeoff

SENIOR DATA ANALYST - 9 MONTH FIXED-TERM CONTRACTHYBRID – LONDON – 2 days per week in officeSALARY – Up to £60,000 + discretionary bonus (3–10%) THE COMPANYA leading UK trade association representing major players in the insurance and long-term savings sector. They influence policy, engage with government and media, and provide insights to member companies across the industry! THE ROLEAs a Senior Analyst, you will:<ul><li>Lead data collection and analysis projects using member and third-party data.</li><li>Deliver reporting, market forecasting, and actionable insights to internal teams and stakeholders.</li><li>Build dashboards and visualisations in Power BI to support decision-making.</li><li>Work on key projects such as diversity & inclusion reporting and annual market data collections.</li><li>Liaise with internal teams and external partners to ensure data is accurate, insightful, and actionable.</li><li>Present findings in committees and stakeholder forums.</li></ul> YOUR SKILLS AND EXPERIENCE<ul><li>Prior experience in a data & analytics role</li><li>Strong SQL, Excel, and data visualisation skills (Power BI preferred).</li><li>Experience with third-party/market data (e.g., surveys, regulatory data).</li><li>Able to generate actionable insights and communicate effectively with stakeholders.</li><li>Strong stakeholder management and collaboration skills.</li></ul> BENEFITS<ul><li>Salary up to £60,000 + discretionary bonus.</li><li>25 days annual leave + birthday off.</li><li>Private medical, gym membership, eye care, and mental wellbeing plan.</li><li>Leadership and professional development opportunities.</li><li>Join a collaborative, values-driven team at the heart of a key UK industry.</li></ul> HOW TO APPLYExpress your interest by sending your CV via the apply link on this page.

Principal Data Scientist London - UK- WIDE TRAVEL EXPECTED If you’re a hands-on Data Science / AI specialist who loves solving real operational problems, this role puts you right at the heart of client delivery. You’ll shape the problems, design and build the solutions, and see your models deployed into live environments – not parked as endless POCs. THE COMPANY This fast-growing consulting firm helps organisations across the public sector, defence and commercial markets deliver measurable improvements in performance through data, AI and digital solutions. They are building a modern AI & Digital capability that sits at the front of transformation programmes, working shoulder-to-shoulder with clients on their toughest operational challenges. THE ROLEAs a Principal Data Scientist, you will blend hands-on technical work with consulting, stakeholder management and practice-building.<ul><li>Identifying high-impact Data Science, Machine Learning, AI and simulation opportunities on client engagements</li><li>Scoping and shaping problems, designing experiments and roadmaps, and then building and deploying models end-to-end</li><li>Leading technical workstreams, managing small teams and ensuring projects are delivered on time and to a high standard</li><li>Translating complex technical concepts into clear, compelling narratives for non-technical senior stakeholders</li><li>Coaching and mentoring consultants to build their capabilities in DS/ML/AI and modern analytics</li></ul> YOUR SKILLS & EXPERIENCE<ul><li>Strong background in Data Science, Machine Learning, AI, simulation or advanced analytics, with clear examples of end-to-end delivery</li><li>Proven ability to shape, not just execute: from defining the problem and designing the solution, through to delivery and measurement of impact</li><li>Consulting capability – comfortable running client workshops, identifying opportunities, influencing senior stakeholders and supporting pitches</li><li>Evidence of measurable commercial or operational impact (e.g. revenue growth, cost reduction, efficiency gains, service improvement)</li><li>Hands-on experience building models and turning prototypes into robust, production-ready solutions</li><li>Experience leading teams or technical workstreams in a project environment</li><li>UK citizenship (for security clearance purposes) and willingness to travel across the UK 2–3 days per week, depending on client needs</li></ul> Nice-to-have experience:<ul><li>Background in a consulting environment or a high-performing in-house DS/AI team</li><li>Exposure to sectors such as defence, public sector and consumer/retail</li><li>Familiarity with modern data and AI tooling such as Python, Spark, Databricks, Azure cloud, and Generative AI platforms (experience with specific tools is helpful but not essential)</li><li>A mix of technical, product and operational experience – comfortable talking both code and business outcome</li></ul>

Lead Product Manager – AI/MLLondon (Hybrid, 2 days onsite)Salary - £100,000 to £110,000 + 15% Bonus The CompanyA consumer-tech business focused on building long‑term financial wellbeing. With AI now a strategic priority, they are investing in new capabilities to bring greater personalisation and intelligent support to their users. The Role • Identify, scope, and define where AI and machine learning can add value across the entire customer journey. • Lead discovery and problem framing for intelligence‑enabled features such as recommendations, personalisation, and automated guidance. • Create system‑level product designs that connect data, decisioning, and user experience. • Shape how cross‑journey capabilities are built and reused across teams. • Work closely with Engineering, Data, and Decisioning partners to ensure scalable, ethical and customer‑focused implementation. • Provide product leadership to teams with emerging AI knowledge, helping them understand how and when to apply ML effectively. • Operate as a senior individual contributor with significant influence across the organisation. Your Skills and Experience • Strong product management experience in B2C consumer apps. • Proven experience delivering AI or ML‑powered features such as personalisation, recommendations, automation, or intelligent guidance. • Skilled in end‑to‑end product discovery, scoping and problem‑solving. • Ability to think in systems and design across journeys rather than isolated features. • Confident influencing engineering, data, and leadership stakeholders. • Comfortable operating independently without people‑management responsibilities. What They Offer • £100,000 to £110,000 base salary depending on experience. • 15% performance bonus. • Hybrid working with two days per week in their central London office. • Pension, private medical, health cash plan, Gympass, cycle to work scheme. • Enhanced parental leave, 25 days holiday plus bank holidays, L and D budget, home office allowance. How to Apply If you are a consumer product leader with strong AI experience, please apply with your CV.

BI ANALYST LONDON/HYBRIDUP TO £60,000 This is a great opportunity to join a global data team that is shaping how a luxury consumer brand understands performance across retail and ecommerce. You will play a key role in building self‑service reporting, delivering meaningful insights, and partnering with stakeholders who rely on data to drive commercial decisions. ROLES AND RESPONSIBILITIES:The BI Analyst will:• Build self‑service Power BI dashboards that are visually clear, easy to use and aligned to business needs.• Deliver deep‑dive analysis across retail and ecommerce performance to identify opportunities, trends and predictive KPIs.• Use SQL to integrate and interrogate large datasets, including newly merged tables and complex data structures.• Support stakeholders in how they self‑serve and embedding best‑practice reporting.• Present insights clearly to global and local teams, translating technical analysis into actionable recommendations.• Own reporting development once the current contract resource transitions off the project. YOUR SKILLS AND EXPERIENCE:The ideal candidate will have the following skills and experience:• Strong commercial experience using SQL to work with large, complex datasets.• Confident building and maintaining Power BI dashboards, including basic DAX.• Experience working on customer, marketing or commercial data• Ability to partner effectively with stakeholders, understand business needs and communicate insights clearly.• Experience or strong interest in the luxury, retail, beauty or wider B2C sector would be advantageous.

Senior Credit Risk AnalystUp to £55,000HybridWest Midlands The CompanyI am hiring a Senior Credit Risk analyst for lending business that leads the way on all types of unsecured lending for a large consumer base across the UK. Within this role you will help drive credit risk strategy using analytics to drive companywide profitability. The RoleAs a Senior Credit Risk Analyst, you will be:<ul><li>Reporting on credit risk insights and trends to senior stakeholders</li><li>Using analytics skills to improve credit risk strategies</li><li>Helping manage the credit risk customer lifecycle</li><li>Spotting trends to find new ways to increase profitability for credit risk</li><li>Developing new credit risk strategies across the business for acquisitions and existing customers.</li><li>Working with and presenting to senior stakeholders daily within the role.</li></ul> Your skills and experience:To be successful as a Senior Credit Risk Analyst, you will need:<ul><li>Strong experience with SAS and/or SQL</li><li>Experience as a credit risk analyst in an analytical position</li><li>Experience developing credit strategies for acquisitions, ECM, or collections.</li><li>Experience working in financial services</li><li>Strong stakeholder management & excellent communication skills</li><li>STEM Degree from a top university</li></ul> BenefitsUp to £55,000 + benefits package

LEAD DATA ANALYST (Performance Marketing)London, Hybrid - 1 Day a week in the officeUp to 65,000 The CompanyThey are a specialist performance marketing and technology agency built around data, engineering, and analytics. They work with some leading tech brands and are growing rapidly! The RoleAs Lead Data Analyst, you will: • Guide the development and deployment of models within cloud environments and marketing platforms.• Partner closely with client‑facing teams to drive measurable impact.• Shape long‑term analytics strategy and contribute to innovation across forecasting, pricing, and advanced analytics.• Support client engagements by advising on best‑practice measurement and experimentation frameworks.<ul><li>Work in cloud environments, deploying models within google/meta ads</li></ul> Your Skills and Experience• Strong SQL capabilities• Practical knowledge of MMM, LTV modelling, and predictive or propensity modelling.• A background in delivering long‑term analytics or modelling projects with clear commercial impact.• Experience with attribution is beneficial but not required. What They Offer• Salary up to 65,000.• Hybrid working• Exposure to major consumer brands and high‑impact, long‑term projects.• Ongoing professional development and a collaborative, data‑driven culture.

Data AnalystUp to £50,000 + BenefitsLondon Harnham is working with a rapidly expanding company in the Green Tech space, as they continue to build out their data capability. They’re looking for a capable Data Analyst who enjoys crafting polished dashboards, digging into SQL, and turning complex datasets into practical insights the business can actually use. THE ROLEIn this position, you will:<ul><li>Work alongside the Senior Data Analyst and Data Engineer to produce reliable, timely reporting that supports the company’s strategic and operational goals.</li><li>Build Power BI dashboards that are both aesthetically strong and functionally intuitive, based on clear stakeholder requirements.</li><li>Write clean, performant SQL to extract and prepare data for analysis.</li><li>Carry out deep-dive and ad hoc analysis for teams across operations, commercial, and marketing</li><li>Translate insights into clear, compelling narratives for audiences with varying levels of technical understanding.</li><li>Deliver Power BI guidance and training, helping teams become more self-sufficient with their data.</li><li>Document processes and project steps clearly, ensuring work is transparent, reproducible, and methodical.</li><li>Collaborate through pair programming and structured testing to maintain accuracy and consistency.</li></ul> YOUR SKILLS & EXPERIENCEWe’re looking for someone with:<ul><li>1-2 years’ experience as a Data Analyst.</li><li>A degree in a quantitative subject (STEM, Maths, Stats, Engineering, etc.).</li><li>Strong SQL capability - must have experience in select, joins, group by, sum/avg/count, CTEs are nice to have</li><li>Demonstrable experience building dashboards using good UX principles, Power BI a preference but not a must have</li><li>A methodical working style with solid organisational and documentation habits.</li><li>Comfort working closely with others on code reviews, data testing, or pair programming.</li><li>Strong attention to detail and a structured analytical mindset.</li><li>Confident communication and presentation abilities.</li><li>A proactive approach, curiosity, and a willingness to continuously learn.</li></ul>

Do you want to apply machine learning to real-world transport data used by millions daily?Have you worked with messy datasets and uncovered insights others miss?Are you ready to shape how a major UK transport network uses data to make commercial decisions? A major UK rail operator is expanding its data capability within the commercial team. Operating over 1,500 services every day, the business relies heavily on understanding passenger behaviour, network demand, and station usage to optimise services and improve customer experience. They are hiring a Data Scientist to join a growing data team responsible for extracting deeper insights from large operational datasets. The role offers the chance to work across predictive modelling, geospatial analysis, and commercial analytics while collaborating closely with stakeholders across the business. Key Responsibilities• Build and deploy predictive models using Python and machine learning techniques• Conduct exploratory analysis across transport and mobility datasets• Perform geospatial analysis to understand customer journeys and station catchment areas• Work with stakeholders to scope and deliver data science projects• Analyse complex datasets including mobile network mobility data• Communicate insights through clear visualisations and dashboards• Collaborate with Data Engineers and Analysts across the data function Key Details• Salary: Up to £52k• Working model: Hybrid – 2–3 days per week (near Waterloo Station)• Tech stack: Python, SQL, Azure, Microsoft Fabric, Power BI• Visa sponsorship: Not available Interested? Please apply below.

Staff Data Scientist &amp;OpenCurlyDoubleQuote; Post SalesLocation: San Francisco (Hybrid)Salary: $200&amp;OpenCurlyDoubleQuote;250k base RSUsThis fast-growing Series E AI SaaS company is redefining how modern engineering teams build and deploy applications. We're expanding our data science organization to accelerate customer success after the initial sale&amp;CloseCurlyDoubleQuote;driving onboarding, retention, expansion, and long-term revenue growth.About the RoleAs the senior data scientist supporting post-sales teams, you will use advanced analytics, experimentation, and predictive modeling to guide strategy across Customer Success, Account Management, and Renewals. Your insights will help leadership forecast expansion, reduce churn, and identify the levers that unlock sustainable net revenue retention.Key ResponsibilitiesForecast &amp; Model Growth: Build predictive models for renewal likelihood, expansion potential, churn risk, and customer health scoring.Optimize the Customer Journey: Analyze onboarding flows, product adoption patterns, and usage signals to improve activation, engagement, and time-to-value.Experimentation &amp; Causal Analysis: Design and evaluate experiments (A/B tests, uplift modeling) to measure the impact of onboarding programs, success initiatives, and pricing changes on retention and expansion.Revenue Insights: Partner with Customer Success and Sales to identify high-value accounts, cross-sell opportunities, and early warning signs of churn.Cross-Functional Partnership: Collaborate with Product, RevOps, Finance, and Marketing to align post-sales strategies with company growth goals.Data Infrastructure Collaboration: Work with Analytics Engineering to define data requirements, maintain data quality, and enable self-serve dashboards for Success and Finance teams.Executive Storytelling: Present clear, actionable recommendations to senior leadership that translate complex analysis into strategic decisions.About YouExperience: 6years in data science or advanced analytics, with a focus on post-sales, customer success, or retention analytics in a B2B SaaS environment.Technical Skills: Expert SQL and proficiency in Python or R for statistical modeling, forecasting, and machine learning.Domain Knowledge: Deep understanding of SaaS metrics such as net revenue retention (NRR), gross churn, expansion ARR, and customer health scoring.Analytical Rigor: Strong background in experimentation design, causal inference, and predictive modeling to inform customer-lifecycle strategy.Communication: Exceptional ability to translate data into compelling narratives for executives and cross-functional stakeholders.Business Impact: Demonstrated success improving onboarding efficiency, retention rates, or expansion revenue through data-driven initiatives.

Director of Data ScienceLocation: Atlanta, GA (Onsite)Salary: $250-350k depending on experienceWe're partnering with a leading private equity&amp;OpenCurlyDoubleQuote;backed consumer platform to hire a Director of Data Science to lead and scale an enterprise-level data science function.This role sits at the intersection of investment strategy, portfolio performance, and advanced analytics, with direct influence on how data science, machine learning, and Generative AI are used to drive value across a large multi-brand ecosystem.The RoleYou'll own the vision, execution, and growth of the Data Science function, leading a high-performing team and partnering closely with senior leaders across strategy, engineering, product, and investment teams.The work spans commercial due diligence, forecasting, pricing, experimentation, marketing analytics, and GenAI tooling, with a strong emphasis on building scalable, production-grade solutions that drive measurable business impact.This is a player-coach role &amp;CloseCurlyDoubleQuote; hands-on where needed, strategic where it counts.What You'll Be DoingLead and grow the Data Science team, setting technical standards and best practicesDefine the long-term roadmap, tooling, and architecture for applied data sciencePartner with investment and portfolio teams on predictive modeling and value creationDrive the adoption and impact of Generative AI and agentic workflowsOwn the full model lifecycle, from problem framing through production deploymentTranslate complex analytics into clear, executive-level insightsWhat We're Looking For10years of applied data science / ML experienceStrong Python and SQL skills, with proven production deployment experienceExperience leading and scaling technical teamsDeep expertise in forecasting, experimentation, pricing, and causal analysisStrong business judgment and executive communication skillsNice to have: consumer / retail / QSR exposure, multi-brand environments, PE or consulting background, advanced degree, and a strong point of view on how GenAI tools are reshaping data science workflows.

Calculate Moving Average	SQL	Easy
Predict Customer Churn	Machine Learning	Medium
A/B Test Significance	Statistics	Medium
Optimize Query Performance	SQL	Hard
Feature Importance Analysis	Machine Learning	Medium
Clean Missing Data	Python	Easy
Neural Network Architecture	Deep Learning	Hard
Calculate Cohort Retention	SQL	Medium
Bayesian Probability	Statistics	Easy
Recommend Similar Products	Machine Learning	Hard

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