Senior Data Scientist

Senior Data ScientistJob Title: Senior Data Scientist - RemoteLocation:United States (US time zones)Experience: 4+ years of applied data science deliveryAbout the RoleYou will work as a forward deployed data scientist: embedded with enterprise clients, turning ambiguous business problems into working analytical and AI solutions, and staying with the work until it runs in their environment. This is a hands-on senior role. You will own problems end to end scoping with stakeholders, wrangling messy client data, choosing methods that fit the constraints, building the solution, and handing it off in a state another team can operate.Some engagements need something built from scratch. Many do not the work is adopting, configuring and extending what already exists: our own products and accelerators, the clients current platform, or a vendor tool. We are looking for people who are comfortable either way, who can tell which situation they are in, and who would rather extend a working solution than rebuild it.We are looking for modern technical builders rather than notebook-only analysts: fluent with Python, SQL, Git, cloud and cloud data platforms, AI-native in how they work, credible in front of a client, and pragmatic about when a simple method is enough and when deeper modelling is warranted.Education and background: a quantitative degree statistics, computer science, engineering, operations research, economics, mathematics, physics, or similar. A masters is preferred. A PhD is welcome but not expected. A strong bachelors candidate is equally of interest where there is clear evidence of applied delivery.What You Will Do * 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 clients existing tools, rather than rebuilding from scratch. * Put something in a stakeholders 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.Required### Engineering and tooling * 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 elses 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.### Data science * 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 val