Lead Data Scientist designs, develops, and scales machine learning, forecasting, optimization, and predictive models. Owns end-to-end lifecycle from problem definition through deployment, monitoring, and improvement. Conducts exploratory analysis, assesses data quality, engineers features, evaluates model performance, and partners with engineering and business stakeholders. Establishes best practices, mentors team members, and communicates technical results to non-technical audiences to drive measurable business outcomes.
The Lead Data Scientist is responsible for developing, implementing, and scaling advanced analytics, machine learning, and artificial intelligence solutions that drive business value. This role partners closely with cross-functional stakeholders to translate complex business challenges into data-driven insights and predictive solutions.
The Lead Data Scientist owns the end-to-end lifecycle of data science initiatives, from opportunity identification and model development through deployment, monitoring, and continuous improvement. This role also contributes to the advancement of data science capabilities by establishing best practices, mentoring team members, and promoting analytical excellence across the organization.
ResponsibilitiesEssential Duties and Responsibilities:
- Design, develop, and implement statistical, machine learning, forecasting, optimization, and predictive models.
- Apply advanced analytical techniques, including regression, classification, clustering, time series analysis, anomaly detection, and other quantitative methods.
- Translate business challenges into scalable analytical solutions that generate measurable business outcomes.
- Conduct exploratory data analysis to identify trends, patterns, opportunities, and risks.
- Assess data quality, completeness, and suitability for analytical and modeling initiatives.
- Design, develop, and validate features that improve model performance and business relevance.
- Lead the full lifecycle of data science solutions, from problem definition and model development through deployment and ongoing optimization.
- Evaluate model performance, accuracy, stability, reliability, and business impact.
- Partner with engineering and technology teams to deploy and operationalize models within business Collaborate with business leaders and stakeholders to define objectives, key metrics, constraints, and success criteria.
- Identify and prioritize high-impact opportunities where data science can improve business performance.
- Communicate analytical findings, recommendations, assumptions, and risks to both technical and non-technical audiences.
Ensure solutions are actionable, interpretable, and aligned with business needs and strategic objectives.
Minimum Education and/or Experience:
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Operations Research, or a related quantitative field required.
- Advanced degree in a quantitative field (preferred)
- 7+ years of progressive experience in data science, machine learning, advanced analytics, or a related field.
- Demonstrated experience developing, deploying, and supporting predictive and machine learning models in production environments.
- Experience leading complex analytical initiatives from problem definition through adoption
- Strong proficiency in Python and/or R for modeling and production-quality code
- Strong SQL skills for data exploration and dataset development
- Experience with forecasting, optimization, pricing, customer analytics, operational analytics, or related business applications.
- Familiarity with generative AI, large language models (LLMs), and emerging AI technologies.
- Experience working in cross-functional environments (engineering, analytics, business teams)
- Ability to communicate complex concepts to non-technical stakeholders
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