Senior Data Scientist

About the role

What you will do

• Translate business problems into analytical problems with client stakeholders, and challenge the framing when needed.
• Build analytical assets using enterprise data, including forecasting, optimization, machine learning, statistical models, and pipelines.
• Configure and extend existing products, accelerators, or client tools when that is preferable to rebuilding.
• Put prototypes, apps, dashboards, or analyses in stakeholders’ hands early and iterate from feedback.
• Ship tested, documented, reproducible, reviewed code that others can reuse.
• Work with data and ML engineers through deployment rather than handing work off prematurely.
• Use LLMs and AI coding tools while maintaining personal statistical judgment and review.
• Review work and mentor less experienced colleagues.

Required background

• At least 4 years of applied data science delivery experience.
• Quantitative degree in statistics, computer science, engineering, operations research, economics, mathematics, physics, or a similar field.
• A master’s degree is preferred; a PhD is welcome but not expected. Strong bachelor’s candidates with clear applied delivery evidence are also considered.
• Strong Python and SQL skills, plus Git, pull requests, code review, terminal workflows, package management, and reproducible environments.
• Working experience with at least one major cloud and a modern data platform such as Snowflake, Databricks, BigQuery, Redshift, Spark, or equivalent.
• Ability to write reusable tested and documented code, work in existing codebases, and independently troubleshoot common data and pipeline problems.
• Practical skills with enterprise data, forecasting, optimization, machine learning, statistical methods, and appropriate validation.
• Ability to assess results through experiment design or quasi-experimental methods and to use AI tools with independent review.
• Understanding of production data and ML concepts, including pipelines, quality, reproducibility, versioning, deployment, and monitoring.
• Ability to scope ambiguous work, communicate assumptions and recommendations, deliver usable outputs, and work within client constraints.

Useful experience

• Strong engineering practices, advanced LLM workflows, and enterprise delivery experience are assets.

Location

• The description states that the role is based in Canada or the United States in US time zones.

About Mphasis