Data Scientist, Sr. (WFH)

About the Role

We are looking for a Senior Data Scientist who can turn complex business challenges into practical analytical and AI solutions. In this forward-deployed role, you will work directly with enterprise clients, understand their needs, and take ownership of solutions from initial scoping through implementation and handoff.

This is a hands-on position for someone who combines strong data science fundamentals with practical software development skills. You will work with messy enterprise data, select appropriate analytical methods, build reliable solutions, and collaborate with data and ML engineers to bring your work into production.

Not every problem requires a new solution. Some projects will involve building from scratch, while others will require adapting existing products, internal accelerators, client platforms, or vendor tools. You should be comfortable working in both situations and able to recognize when extending an existing solution is more effective than starting over.

The ideal candidate is proficient in Python, SQL, Git, cloud technologies, and modern data platforms. You should also be comfortable using AI tools in your daily workflow while applying independent judgment, validating results, and communicating clearly with technical teams and business stakeholders.

Key Responsibilities

•Work directly with client stakeholders to understand business challenges, translate them into analytical problems, and challenge assumptions when necessary.

•Build reliable analytical solutions using forecasting, optimization, machine learning, and statistical modeling techniques.

•Work with existing products, accelerators, client platforms, and vendor tools, configuring and extending them where appropriate rather than rebuilding unnecessarily.

•Develop early prototypes, applications, dashboards, and analyses to gather stakeholder feedback and improve solutions through iteration.

•Write reusable, tested, documented, reviewed, and reproducible code that other team members can maintain.

•Collaborate with data engineers and ML engineers throughout implementation and deployment, ensuring solutions are ready for practical use.

•Use LLMs and AI coding tools to support development, analysis, debugging, testing, and documentation while retaining responsibility for technical decisions and final reviews.

•Review colleagues' work and mentor less experienced team members to strengthen overall delivery quality.

Required Qualifications

•At least 4 years of applied data science delivery experience.

•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 ability to select appropriate techniques and validate results rigorously.

•Experience evaluating model performance using defensible methods, including proper backtesting and out-of-time validation for time-series problems, as well as experiment design or quasi-experimental methods where appropriate.

•Regular use of LLMs or AI coding tools for coding, analysis, debugging, testing, and documentation, with the ability to identify, correct, and validate inaccurate or unreliable outputs.

•Basic understanding of production data and ML practices, including pipelines, data quality, reproducibility, versioning, deployment, and monitoring.

•Strong proficiency in Python and SQL, with the ability to write code suitable for use and maintenance by other team members.

•Experience with Git, pull requests, code reviews, terminal workflows, package management, and reproducible development environments.

•Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.

•Experience with a modern data platform such as Snowflake, Databricks, BigQuery, Redshift, Spark, or an equivalent technology.

•Ability to turn exploratory analysis into reusable, tested, and documented code.

•Comfort working with existing codebases and products, understanding how they function, and extending them without disrupting existing functionality.

•Ability to independently troubleshoot common data, query, environment, pipeline, and data-quality issues.

Preferred Skills and Experience

•Experience translating ambiguous stakeholder requests into clearly scoped work, with realistic expectations about feasibility and timelines.

•Strong communication skills, including the ability to explain assumptions, limitations, findings, and recommendations to technical and business audiences.

•Experience presenting analytical results through lightweight applications or dashboards using tools such as Streamlit, Dash, Plotly, or BI platforms, or through clear, well-supported written reports.

•Ability to work effectively within client environments, including their existing tools, access restrictions, and changing priorities.

•Strong software engineering practices and experience with advanced LLM workflows.

•Experience delivering analytical or AI solutions in enterprise environments.

Work Arrangement

This is a fully remote position based in the USA. The role involves direct engagement with enterprise clients and collaboration with data science, data engineering, and ML engineering teams throughout solution delivery.