Sr TA Data Analyst

ABOUT THE ROLE

The Talent Acquisition (TA) Analytics team’s goal is to transform recruiting from a reactive function into a proactive, predictive powerhouse. A successful candidate is a strategic thinker who is flexible with shifting project priorities and thrives in a fast-paced environment. You demonstrate the confidence to tackle complex, ambiguous problems and the ability to partner effectively with a wide variety of stakeholders.

We seek a highly motivated, energetic self-starter to work directly with TA leadership. Your mission is to ensure they have the data, predictive models, and insights necessary to make informed decisions-often guiding them toward what they need to know, rather than just what they are asking for. You will proactively identify opportunities to enhance data quality, evaluate new recruiting technologies, and unlock insights that shape how we build our teams.

RESPONSIBILITIES

• Predictive Analytics & Modeling: Utilize Python or R to build predictive models that forecast pipeline health, candidate conversion, time-to-fill, and long-term candidate quality.
• AI & Tool Evaluation: Act as the analytical gatekeeper for our recruiting tech stack. Evaluate the ROI, efficacy, and potential bias of new AI tools and automated recruiting workflows.
• Strategic Metric Definition: Drive the analytics strategy for modern TA disciplines. Define and track complex metrics such as Quality of Hire (QoH), skills-based hiring efficacy, and internal talent mobility.
• Analytics Engineering & Modeling: Perform robust data modeling, transforming raw system data into structured models for visualization. Consult directly with data warehouse engineers to improve system integrations and data pipelines.
• Data Visualization Strategy: Lead our visualization and reporting ecosystem. Design intuitive dashboards from scratch and apply advanced design tactics across various BI tools, shifting stakeholders from looking at descriptive data (what happened) to prescriptive insights (what to do next).
• Ad-Hoc Consulting: Support complex, ad-hoc analytics requests from partners within TA and the broader People Organization.

REQUIREMENTS

• BS/MS in Engineering, Computer Science, Math, Economics, Statistics, or another highly quantitative discipline.
• Minimum 3 years of work experience in data analysis, data science, analytics engineering, or similar technical roles.
• Proficiency in data manipulation and statistical modeling using Python or R.
• Advanced SQL skills and exposure to modern data stores/querying frameworks (MySQL, BigQuery).
• Proficiency with analytics engineering workflows and version control platforms (Dataform, dbt, GitHub).
• Strong proficiency with enterprise data visualization tools (e.g. Looker, Power BI, Tableau) and a deep understanding of dashboard design, visualization strategy, and data storytelling tactics.

PREFERRED QUALIFICATIONS

• Cross-Industry Perspective: Proven ability to dive into new data domains and learn them quickly. Note: Prior HR or TA analytics experience is not required; candidates bringing analytical best practices from outside the People space are highly encouraged to apply.
• Experience auditing AI/machine learning tools for fairness/bias or assessing software vendor ROI.
• Familiarity with Google Cloud Platform (GCP).
• Exceptional data storytelling skills: the ability to convey complex technical or statistical concepts to non-technical business leaders clearly and persuasively.
• Excellent troubleshooting and problem-solving abilities with a tenacious commitment to finding the root cause of data discrepancies.
• Comfortable taking absolute ownership of projects from requirements gathering to final deployment.

For San Francisco, CA-based roles: The base salary range for this role is USD $131,000 per year - USD $145,000 per year.

You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.