AI Engineering Lead responsible for infrastructure and development of AI agents in product analytics. Managing tools, standards, and governance for a team supporting 60+ products.
About the role
- Data Scientist at LawDepot optimizing the Checkout experience through data-driven decisions. Involves statistical analysis, experiment design, and collaboration to enhance revenue growth.
Responsibilities
- Explore and analyze both experimental data and day to day user data to understand performance across the Checkout experience
- Own end to end analytical initiatives, identifying and executing on revenue opportunities informed by past experimentation, competitive analysis, and market trends
- Design, evaluate, and monitor controlled experiments, identifying follow up optimization opportunities
- Create baseline models and establish experiment metrics to measure uplift, validate hypotheses, and inform revenue growth decisions
- Translate behavioural insights into clear business recommendations tied to subscription valuation and monetization
- Develop SQL queries and Python code for data extraction, analysis, and automation
- Design dashboards that enable self-service reporting and support decision-making
- Build clear, actionable data visualizations that make insights accessible to stakeholders
- Apply appropriate statistical and modeling techniques based on the problem and available data
- Create and initiate monetization projects based on learnings from past initiatives, competitive analysis, and market trends
- Contribute to improving data tracking, event quality, and attribution logic over time
- Collaborate with analysts, developers, and product stakeholders to ensure accurate tracking and measurement
- Present findings in weekly team meetings and contribute to prioritization decisions
Requirements
- Post secondary education in Data Science, Business, Finance, Economics, Mathematics, Statistics, Computer Science, or a related field
- Solid foundation in statistical analysis, including hypothesis testing and interpreting experimental results
- Ability to execute well-scoped analytical tasks and models under minimal guidance, contributing accurate and timely insights to business problems
- Proficiency in SQL, including writing efficient queries with joins, aggregations, and filtering logic
- Familiarity with Python for data analysis and visualization (ex. pandas, Matplotlib)
- Proficiency in Microsoft Excel, including Pivot Tables, formulas, and data visualization
- Strong communication skills and ability to explain complex analysis clearly
- Proactive mindset with a willingness to take ownership
- Curiosity and a natural tendency to dig deeper into unexpected results
Benefits
- Comprehensive health and dental benefits, plus an additional Health Care Spending Account
- Great work life balance (37.5 hour work weeks, flexible schedules, ability to bank hours)
- Three weeks paid vacation, plus bonus personal days
- Continuous learning opportunities, including LinkedIn Learning subscriptions and training budgets
- Company share plan
- Free catered lunches for the entire office
- Monthly social events for team members to enjoy (think axe throwing, rock climbing, board games, food trucks, trivia contests, and charitable activities)
- Casual work environment
- Personal offices for focused work and to have your individual space (plus collaborative workspaces for when you want to meet with the team)
- Free and easily accessible parking
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