Senior Data Scientist

Role Overview

• Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions.
• Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria.
• Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems.
• Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights.
• Develop and maintain model documentation, technical specifications, and implementation plans.
• Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence.
• Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions.
• Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact.
• Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics.
• Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk.
• Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability.
• Perform model validation, testing, and performance assessments prior to production deployment.
• Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives.
• Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes.
• Define key performance indicators and success metrics for machine learning and AI initiatives.
• Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods.
• Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks.
• Use experimentation and data-driven insights to guide product, operational, and strategic decisions.
• Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments.
• Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions.
• Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs.
• Support the creation of scalable, maintainable, and reliable AI and machine learning solutions.
• Ensure model deployment processes align with engineering best practices and operational requirements.
• Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions.
• Communicate complex analytical findings and technical concepts to both technical and non-technical audiences.
• Present recommendations, insights, and model performance results to leadership and project teams.
• Support technical reviews, project planning, and delivery activities across cross-functional initiatives.
• Contribute to knowledge sharing, documentation, and best practices within the data science organization.
• Provide technical guidance and mentorship to junior team members and peers as needed.

Requirements

• Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field; Master's degree preferred.
• 7+ years of experience in data science, machine learning, advanced analytics, or a related field.
• Demonstrated experience developing and deploying machine learning models in production environments.
• Strong foundation in statistics, hypothesis testing, experimental design, and predictive modeling.
• Experience working with large datasets and distributed data processing environments.
• Proficiency in Python, SQL, and common data science and machine learning frameworks.
• Experience communicating analytical findings and recommendations to business and technical stakeholders.
• Proven ability to lead projects and collaborate effectively across cross-functional teams.

Tech Stack

• Python
• SQL

Benefits

• stock options
• range of medical benefits
• dental benefits
• vision benefits
• financial benefits
• generous PTO
• stipends for professional development
• wellness benefits