EPAM Data Scientist interviews reported by candidates combine an HR screen, ML and deep learning questions, Python coding and a manager or senior conversation. Experiences vary by team and office, and scheduling has been a pain point.
$100K
Avg. Base Comp
$120K
Avg. Total Comp
5 rounds
Typical Rounds
Not reported
Process Length
Candidates describe EPAM Data Scientist interviews as technically theory-heavy, but inconsistent from team to team. Four reports are available, all ending without an offer, so treat this as a sample rather than a fixed format.
Two candidates describe an HR screen followed by a technical assessment. One of them covered AI, ML and deep learning concepts, and the other was a traditional ML exam. In the first account, a second round with a senior data scientist went deep into the candidate's own AI and ML projects, then moved to deep learning, generative AI concepts and LeetCode-style Python. A third round with another data scientist was similar, followed by a managerial round with technical and cultural-fit questions. A senior-level candidate reports a 90-minute interview centered on transformers and LSTMs, including how to implement positional encoding in an LSTM, plus a stakeholder-conflict question and a final conversation with the Head of AI.
One account involving the Odysseus team points to a different flavor. Candidates there reportedly faced live SQL, R or Python coding and healthcare data model questions around OHDSI and OMOP, such as defining cohorts.
Be ready to explain your past projects in real depth and to write working code for simple problems. One candidate was told to expect system design and instead faced NLP-heavy questions, so prepare ML and NLP fundamentals broadly rather than relying only on the outline you are given. Candidates also report rescheduled calls, a no-show interviewer and days of silence after a screen, so confirm times and next steps in writing.
Synthesized from 4 candidate reports by our editorial team.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report an opening conversation about their background and past projects, which two describe as supportive. One candidate says it was a light preliminary screen and was promised an online assessment afterward. Expect to summarize your experience clearly, and ask for firm dates on next steps.
Two candidates report a technical assessment after the HR stage. One describes AI, ML and deep learning concept questions, and a senior-level candidate describes a heavily theoretical traditional ML exam that differed from the website description. Review ML fundamentals broadly.
Candidates report technical rounds that dig into their own AI and ML projects, with deep learning and generative AI concepts, and LeetCode-style Python problems. A senior-level candidate describes a 90-minute round on transformers, LSTMs and positional encoding. Another report mentions SQL, R and OHDSI/OMOP questions, which may be specific to the Odysseus team.
Reported final-stage conversations include a managerial round with technical and cultural-fit questions, a behavioral question about conflicting stakeholder requests, and a final interview with the Head of AI. Tone and expectations varied, and one candidate felt the bar was set too high for their experience level. Prepare concrete examples of prioritization and project ownership.
