Senior People Data Scientist, Instacart

• The ideal candidate should have robust analytical skills, be proficient in machine learning, and have experience in using HR data to provide insights that assist with business and employee decisions
• 5+ years of experience in data science roles, preferably in HR or People Analytics
• Ability to write complex, efficient, and eloquent SQL queries to extract data
• Proficiency in programming languages such as Python or R for machine learning and statistical analysis
• Experience building and deploying machine learning models (e.g., decision trees, regression, clustering)
• Strong statistical and problem-solving skills, with an emphasis on feature engineering and modeling
• Familiarity with Snowflake or equivalent data warehousing platforms for model integration
• Excellent communication skills to convey technical findings to non-technical audiences

Desirable

• We are seeking a highly skilled and motivated Senior Data Scientist to join our People Analytics team
• This position focuses on developing advanced predictive and prescriptive models to drive strategic insights for our organization
• Our People Analytics team, part of the broader People Services Delivery organization, plays a critical role within the overall People Team
• We are a small but impactful function dedicated to leveraging data to drive strategic decisions and create value for the business
• Collaborating closely with internal partners and cross-functional stakeholders, we provide insights and solutions that align with our company’s goals and enhance the employee experience
• Joining our team means bringing curiosity, technical expertise, and creativity to help shape the future of our workforce through data-driven decision-making
• Build predictive models to forecast trends such as employee turnover, workforce engagement, and talent needs
• Develop prescriptive analytics solutions to inform HR strategies and optimize decision-making processes
• Analyze large datasets from various systems (e.g., Workday, Greenhouse) to uncover actionable insights
• Implement machine learning frameworks for solving HR-related challenges
• Harness insights from unstructured or qualitative data sources, such as performance reviews, annual engagement survey comments, or open-text survey responses
• Develop natural language processing (NLP) models to analyze text data and uncover trends, sentiment, or organizational themes
• Collaborate with stakeholders to define business problems, translate them into technical solutions, and communicate results effectively
• Integrate predictive outputs into Snowflake or visualization platforms like Tableau and Looker
• Partner with HR and data engineering teams to ensure seamless data pipeline management and integrity
• Maintain a strong focus on data accuracy, security, and compliance