IBM Data Scientist Interview Guide

IBM is a multinational technology company founded in 1911 and operates in over 170 countries worldwide. Today, IBM offers a wide spectrum of products and services, including software solutions, hardware architecture (server and storage architecture), business and technology services, and global financing solutions.

As a data driven-company, IBM understands the importance of data and data analytics at every layer of the organization to drive better business decisions. Also, a leading provider of Analytics and Cloud-based solutions, IBM offers a full stack of cloud-based products and services spanning data analytics, storage, AI, IoT, and blockchain.

IBM’s data science interviews questions typically focus on a range of skills and topics.

A data scientist’s role in any enterprise analytics team ranges from identifying opportunities that offer the greatest insights to analyzing data to identify trends and patterns, building pipelines and personalized machine learning models for understanding customers’ needs, and making better business decisions.

At IBM, the term data science covers a wide scope of data science-related related jobs (Data Analyst, Data Engineer, Data Scientist, and Research Analyst) and roles can include uncovering insights from data collection, organization, and analysis, laying the foundations for information infrastructure, and building and training models with significant results. Roles are sometimes specific to teams and products assigned, and sometimes they can be more specialized, like the IBM Analytics Consulting Service for internal and external clients.

IBM’s data scientists are placed in teams working on IBM products and services such as IBM Watson Studio, IBM Cloud Pak, IBM Db2, IBM SPSS, IBM Infosphere, etc.

Required Skills:

IBM is a data-driven organization, and data science is a big deal. Data scientist roles at IBM require field specialization, and so IBM hires only highly qualified individuals with at least 3 years (5+ years for senior-level roles) of industry experience in data analysis, quantitative research, and machine learning applications.

Other basic qualifications include:

• BSc/Masters/Ph.D. in Statistics, Mathematics, Computer Science, and any other STEM-related fields.

• Extensive experience with statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets.

• Advance knowledge in creating and using advanced machine learning algorithms and statistics such as regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.

• Experience with classical approaches to machine learning and linear algebra, including Support Vector Machine (SVM) for linear categorization and Singular Value Decomposition (SVD) to reduce data dimensionality.

• Over three years of experience working with data visualizing and reporting tools such as Excel, PowerBI, Tableau, etc.

• Extensive industry experience working with distributed data or computing tools such as Hive, Spark, MySQL, etc

• Experience in natural language processing, text analytics, data mining, text processing, or other AI subdomains and techniques

• Sound understanding of data analytics infrastructure and data engineering processes, including data storage and retrieval, ETL pipelines, Docker, Kubernetes, etc.

• Background knowledge of software engineering practices such as version control, continuous delivery, unit testing, documentation, release management

Data Scientist Teams at IBM

Like most big tech companies, IBM has a plethora of products and services, and there are many departments and teams of high-qualified and specialized professionals working on developing new products and improving existing ones.

IBM’s data scientists work in teams and may sometimes work cross-functionally with internal teams. Specific functions may vary across teams, but the available data scientist role ranges heavily from lightweight data analytics to machine learning/deep learning.

Listed below are some of the data science teams at IBM and the specific data scientist roles in the teams:

• User Experience Research & Analytics: Roles include analyzing large data sets from multiple repositories, including primary research, behavioral data, and databases such as AWS S3, Azure, MongoDB, SQL, or NoSQL to create predictive and prescriptive models and to extract actionable insights. Roles also include developing automated reports and dashboards and communicating findings with stakeholders such as Executives, Project managers, and Design teams.

• IBM Global Technology Services (GTS) Analytics Team: This team develops and builds innovative AIOPS solutions using advanced analytics and machine learning models to automatically analyze big data collected from various IT operations tools and devices to spot and rectify issues in real-time. Data scientists in this team leverage Deep learning and LSTM models to detect any anomaly in real-time and prevent downtimes automatically.

• IBM Q Start team: Data scientist here, working with research and algorithm experts to implement quantum approaches to data-processing, running numeric, and data visualization.

• Software Development & Support: Data scientists in this team are responsible for expanding and optimizing data models, prediction algorithms, correlation algorithms, and text analytics models. As a data scientist in this team, you will also be responsible for Natural Language Processing (NLP) for entities and text analytics in human-generated tickets using Natural Language Classification and RNN algorithms.

• IBM SME: Roles in this team involve leveraging analytics and deep learning models for predicting emerging trends and providing recommendations for optimizing business results.

• IBM Global Business Services (GBS): This team enables IBM’s enterprise clients to make better and smarter business decisions by leveraging business acumen and predictive machine learning models.

• IBM Client Innovation Centre (CIC): Data scientists in this team leverage a variety of machine learning techniques, including clustering, decision tree learning, artificial neural networks, etc, and advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, and proper usage, etc.) to create solutions and provide actionable insights for business.

The interview process at IBM starts with taking an online coding challenge, “HireVue”. After this is an initial phone screen interview with a recruiter or HR about resumes and past relevant projects, this is followed by a technical screen that may consist of various coding questions ranging from basic python and SQL to medium-level Algo questions. The last stage is the onsite interview consisting of 3 interview rounds.

Online Challenge

A 5-hour online data challenge test on the HireVue platform. This challenge includes mid-level difficulty questions around behavior, machine learning, and statistics. Candidates will have to answer 13 questions in all, with some requiring video response, short essay writeup, oral explanation, and coding solutions.

Initial Screen

This is an exploratory interview with HR or a hiring manager. Questions in this interview revolve around your resume and background experience as it aligns with the job role you are applying for.

Technical Screen

Unlike the initial interview, the technical interview is a lot more in-depth. You are also asked about past projects, with questions like:

• What challenges did you face?
• How did you overcome those challenges?
• What techniques or method did you use?
• What machine learning algorithms were used in your project?
• How did you choose the parameters?

There are also many coding questions and discussions on machine learning theories and concepts.

Onsite Interview

The IBM data scientist onsite interview consists of 2 to 3 interview rounds with a panel of interviews comprising of senior data scientists, managers, and IBM staff from Design, Statistics and Machine Learning, and Management.

Questions span statistical concepts, machine learning concepts and methods, big data and frameworks, and situational-behavioral interview questions. Statistics questions, for the most part, are case-study-based. You can also expect questions like “How would you attempt to solve a data science problem?”, “Describe prior projects/datasets that you worked with.”, and “Tell me about a time…”.

The overall onsite interview process looks a lot like this:

• Statistics Interview
• Machine learning/Coding Interview
• Behavioral Interview

Note: Questions in the behavioral interview are mostly around role-related past projects and experiences mentioned in your resume.

Typically, interviews at IBM vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

Interview Tips for IBM Data Science Roles

Like every standard data scientist interview, the IBM data scientist interview comprises the length and breadth of data science concepts. Questions cover multivariate statistical and machine learning algorithms including:

• Principal Component Analysis
• discriminant analysis
• linear and logistic regression
• k-nearest neighbors
• classification and regression trees
• neural networks
• predictive and prescriptive models
• multivariate regression
• cluster analysis.

It helps to study basic statistical and machine learning models and practice coding on a whiteboard to familiarize yourself with the onsite interview. Visiting Interview Query and practicing IBM data science interview questions can help you ace the technical section of the onsite interview.

Remember, IBM relies heavily on situational questions, so you may come across questions like “Tell me about a time…”, “How do you…”, “How will you solve…”, and “Describe a project you…”. It helps to relate every concept with past projects that you worked on and how using such concepts or techniques helped you overcome challenges.

Practice for the IBM Data Scientist interview with these recently asked interview questions.

• Estimate the value of Pi using the Monte Carlo Algorithm.
• What is deep learning?
• What is a standard deviation?
• What is the difference between precision/specificity?
• What is your vision for being a Data Analyst?
• Define a confidence interval?
• Explain the importance of a p-value?
• What languages are you familiar with? (python, java, etc)
• What’s the difference between Supervised vs. Unsupervised machine learning?
• What is precision? What is specificity? What is sensitivity/recall?
• How many years of Python programming do you have?
• Describe precision and recall.
• Why do you want to work for IBM?
• What is the p-value?
• What do you know about Tensorflow?
• How do you evaluate the performance of a regression prediction model as opposed to a classification prediction model?
• Difference between supervised & unsupervised learning.
• Why do you think your background is a good fit for IBM
• How do you deal with a missing value
• What is the matrix used to evaluate the predictive model?
• What are the relationships between the coefficient in the logistic regression and the odds ratio?
• How do you validate a machine learning model?
• How do you implement Fibonacci in python? Why is loop better than recursion?

Find more IBM Data Scientist interview questions below: