As a Data Scientist III, you will leverage theory, data, and research to solve business problems. You will support data science and analytics efforts across multiple areas of the business including Sales, Marketing, Finance, HR, and related functions. You will contribute to building and improving measurement and reporting processes. To this end, you will help teams access insights needed to operate effectively.
This role will be responsible for drawing insights from large data sets, defining and implementing key model performance indicators, and for communicating insights and trends to support business decision-making, as it relates to data science-enabled decisions. This role will work with datasets relevant to assigned projects and business areas and be responsible to work closely with business stakeholders on measurement, success metrics, and analytics. Effectiveness in this position will require an understanding of technical methods and data engineering necessary to build and implement data science models, as well as knowing general industry trends, business objectives, and workforce dynamics. You will use data to develop insights, forecasts, metrics, dashboards and recommendations to inform decisions about our operations and go-to-market strategy.
What You'll Do (Essential Functions)- Own data science projects end-to-end within your assigned business area. Scope ambiguous problems and define success metrics, through model development, deployment, and ongoing monitoring.
- Translate business questions from Sales, Marketing, Finance, HR, and related functions into well-defined data science problems with clear KPIs, deliverables, and timelines.
- Assess the feasibility, expected impact, and tradeoffs of proposed initiatives to inform prioritization and scoping decisions with cross-functional partners.
- Design and conduct rigorous statistical analyses and build robust predictive models to answer complex business questions, selecting appropriate methods (e.g., regression, time series, survival/attrition, hypothesis testing, graph and network analysis) for the problem at hand.
- Optimize models for performance, interpretability, and generalization using techniques such as regularization, cross-validation, and ensemble methods.
- Define model evaluation plans that cover accuracy, fairness, drift, and business impact, and interpret results in context for stakeholders.
- Design and implement production-ready data and model pipelines that handle large-scale datasets, leveraging distributed processing systems (e.g., Snowflake, Databricks, Google Cloud Platform) and building reusable, modular components.
- Write readable, well-structured, modular code using standard data science libraries, and ensure your work is tested, documented, and maintainable by others.
- Participate in code and design reviews, giving and receiving constructive feedback to raise the quality bar across the team.
- Contribute to shared practices, tooling, and documentation that improve how the data science function operates.
- Share knowledge with teammates and provide informal guidance to more junior colleagues on modeling, code, and analysis approaches.
The information in this job description represents a summary of the role and is not intended to be a comprehensive list of job duties. Responsibilities and duties of the position may change without notice at the Company's discretion.
What You Bring (Required Qualifications)- Master's degree in a STEM or quantitative field (statistics, computer science, mathematics, economics, engineering, or related).
- 3+ years of professional experience building and deploying data science or machine learning solutions, including at least one production deployment.
- Demonstrated experience translating business questions into data science problems and communicating findings to technical and non-technical audiences.
- Proficiency in Python and SQL, and experience with data visualization tools such as Tableau.
- Experience working with cloud data and ML platforms such as Snowflake, Databricks, or Google Cloud Platform.
- Working knowledge of applied statistical methods and machine learning techniques (e.g., regression, classification, time series, cross-validation, model evaluation).
Preferred Qualifications- Doctoral degree in a STEM or quantitative field (statistics, computer science, mathematics, economics, engineering, or related).
- Familiarity with MLOps practices - model versioning, monitoring, drift detection, CI/CD for ML.
- Experience designing and maintaining dashboards for operational or executive audiences.
- Experience presenting analyses to senior technical and non-technical audiences.
- Exposure to Sales, Marketing, Finance, or HR analytics domains.
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