Note: The job is a remote job and is open to candidates in USA. Mphasis is hiring a Senior Data Scientist to work directly with enterprise clients, turning ambiguous business problems into deployable analytical and AI solutions. The role covers end-to-end delivery, from scoping and data preparation through building, validating, deploying, and handing off solutions, while also extending existing products and tools.

Responsibilities

• Partner directly with client stakeholders to translate business problems into analytical problems — and push back when the framing is wrong
• Build reliable analytical assets on messy enterprise data: forecasting, optimization, ML, statistical models, and the pipelines around them
• Just as often, work with what is already there — configure and extend our existing products and accelerators, or the client’s existing tools, rather than rebuilding from scratch
• Put something in a stakeholder’s hands early — a prototype app, a dashboard, a clear analysis — and iterate from real feedback
• Ship code others can reuse: tested, documented, reproducible, reviewed
• Work alongside data engineers and ML engineers through to deployment, rather than throwing work over the wall
• Use LLMs and AI coding tools to move faster, while keeping statistical judgment and final review your own
• Raise the level of the people around you: review work and mentor less experienced colleagues

Skills

• 4+ years of applied data science delivery
• A quantitative degree — statistics, computer science, engineering, operations research, economics, mathematics, physics, or similar
• Strong proficiency in Python and SQL, at a level where your code goes to other people rather than only into your own notebook
• Comfortable with Git, pull requests, code review, terminal workflows, package management, and reproducible environments
• Working experience on at least one major cloud (AWS, Azure, or GCP) and with a modern data platform such as Snowflake, Databricks, BigQuery, Redshift, Spark, or equivalent
• Able to turn exploratory analysis into reusable, tested, and documented code
• Comfortable working inside an existing codebase or product as well as starting from a blank page — reading someone else's code, understanding how it works, and extending it without breaking it
• Able to independently troubleshoot common data, query, environment, and pipeline issues — failed jobs, broken queries, bad joins, package conflicts, data-quality problems — without immediately requiring an engineer
• Strong practical data science skills, including working with messy enterprise data and translating business problems into actionable analysis
• Working knowledge of forecasting, optimization, machine learning, and statistical methods — with the judgment to know when a simple method is sufficient, and the discipline to validate honestly (proper backtesting and out-of-time validation for time series, not just a random train/test split)
• Able to answer “did this actually work?” with defensible evidence: experiment design and read-out, or quasi-experimental methods when a clean test is not possible
• Regular user of LLMs or AI coding tools for coding, analysis, debugging, testing, and documentation, while maintaining independent judgment. You should be able to explain what AI output you accepted, rejected, rewrote, and tested, and how you spot output that is plausible but wrong
• Basic understanding of production data/ML concepts, including pipelines, data quality, reproducibility, versioning, deployment, and monitoring
• Able to take an ambiguous request from a business stakeholder and turn it into a scoped piece of work, with a view on what is feasible in the time available
• Able to clearly communicate assumptions, limitations, findings, and recommendations to both technical and business stakeholders — in writing and in the room
• Able to make results usable: a lightweight app or dashboard (for example Streamlit, Dash, Plotly, or a BI tool) or a clean, well-argued document, depending on what the audience needs
• Comfortable working in someone else's environment and under their constraints — client tooling, access restrictions, and shifting priorities — without stalling
• A master's is preferred
• Strong engineering practices, experience with advanced LLM workflows, and experience delivering in enterprise environments are an asset

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