Senior People Data Scientist

Overview

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. 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.

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.

About the Job

• 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.

About You

Minimum Qualifications

• 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

Preferred Qualifications

• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related field.
• Experience with HR-specific systems, such as Workday, Greenhouse, or LMS platforms.
• Proven track record of working with HR data and metrics (e.g., turnover, performance, DEI metrics).
• Familiarity with data visualization tools.
• Understanding of data governance and compliance standards

Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here. Currently, we are only hiring in the following provinces: Ontario, Alberta, British Columbia, and Nova Scotia.

Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here.

For Canadian based candidates, the base pay ranges for a successful candidate are listed below.

CAN

$161,000—$170,000 CAD