Data Scientist (Product, Moloco Commerce Media)

Moloco; Menlo Park (CA); On-site; USD 150,000 - 200,000; Full time

Benefits offered by this job

Health insurance

L&D stipend

Lunch provided

Parental leave

Unlimited vacation

Home office reimbursement

Job summary

Moloco is seeking a Data Scientist for the MCM Data Science team in Menlo Park to partner with Product, Engineering, and GTM teams. You will shape success definitions, evaluate shipped features, and guide investment decisions based on data.

You will collaborate with product managers from concept through experimentation, readouts, and rollout, communicating insights to leadership and marketplace partners. Strong stats, Python, and SQL skills are essential.

Qualifications

• 3+ years in data science, analytics, or related field.
• Advanced degree with applied experience acceptable.
• Strong communication of technical findings to non-technical stakeholders.

Responsibilities

• Partner with Product on strategy and prioritize data-driven opportunities.
• Define success metrics, north star and guardrails for new capabilities.
• Design experiments and evaluation methods for launches and iterations.
• Analyze large-scale ad-tech data (ranking, retrieval, bidding, pacing).
• Translate analyses into actionable recommendations for leadership.
• Uphold data science excellence and best practices.

Skills

Python

SQL

Experimentation

Statistics

Data storytelling

Education

Bachelor's/Master's/PhD in quantitative field

Tools

None

Job description

• Moloco Commerce Media (MCM) enables marketplaces to leverage recent advances in AI to deliver highly relevant ads powered by first-party data
• MCM has established strong product-market fit and is a key growth engine for Moloco as we expand our footprint with leading global and regional commerce platforms
• As a Data Scientist on the MCM Data Science team, you will partner closely with Product, Engineering, and Go-To-Market teams to shape how MCM defines success, evaluates what it ships, and decides where to invest next
• You will work alongside product managers from the earliest stages of a new capability through experimentation, readout, and the decisions that follow
• Your work will directly inform which features MCM builds, how we know they work, and how we communicate their value to leadership and marketplace partners
• Partner with Product on strategy: Act as an independent thought partner to product managers, proactively surfacing opportunities, sizing them with data, and informing roadmap prioritization. You will bring your own point of view on what to build and why
• Define success metrics for new product initiatives: Establish how MCM measures new capabilities, including north star and guardrail metrics, the trade-offs they encode, and the thresholds that guide ramp decisions. Align Product and Engineering on these frameworks before launch
• Drive evaluation and experimentation: Design experiments and evaluation methodologies for product launches, including cases where standard A/B testing is insufficient. Analyze results rigorously and translate them into clear ship, iterate, or stop recommendations
• Conduct deep-dive analyses: Use large-scale data across our ad-tech stack (ranking, retrieval, bidding, pacing) to understand how product changes affect advertiser outcomes and marketplace dynamics, and turn findings into actionable recommendations
• Communicate insights clearly: Distill complex analyses into narratives that product, engineering, and non-technical stakeholders can act on. Deliver launch readouts and contribute the data narrative to product reviews
• Uphold data science excellence: Apply best practices in analysis, modeling, and experimentation, and contribute to a strong data culture within the MCM team

Benefits

• Comprehensive health and wellness: Competitive health (100% coverage for you), dental, vision and life insurance for you and your family. In addition, enjoy membership to Calm, Headspace, Spring Health, as well as fitness and health reimbursements
• Professional development: $2,500 Learning and Development stipend annually to grow yourself professionally
• Food and snacks: Enjoy lunch on us. We bring lunch in for employees everyday, In addition, our kitchens are filled with healthy snacks and beverages for you to enjoy
• Paid parental leave: Our parental leave for primary and secondary caregivers have you covered when you welcome a new child into your family
• Unlimited vacation: We encourage team members to take time off to relax and recharge with our flexible paid time off policy. We also offer leave time for various reasons
• Financial benefits: We provide equity and generous retirement benefits that help you prepare for your future. In addition, home office setup and phone, internet and transportation reimbursements

Experience partnering with product or business stakeholders: a track record of defining success metrics, running experiments, and influencing decisions with dataStrong proficiency in Python and SQL, with experience analyzing large-scale datasets3+ years of industry experience in data science, analytics, or a related field (or an advanced degree with equivalent applied experience)Solid foundation in statistics, including experimental design, hypothesis testing, and various statistical analysis techniques such as regressionBachelor’s, Master’s and/or PhD in a quantitative discipline (e.g. Statistics, Operations Research, Economics, Mathematics, etc.)Excellent communication skills with experience presenting technical findings to non-technical stakeholdersAbility to build strong relationships and collaborate effectively in a global, cross-functional organizationYou are a strong collaborator who builds trust with product, engineering, and commercial stakeholdersYou operate independently in ambiguity, turning an open-ended product question into a structured analytical plan without waiting for a specYou are energized by fast-moving, early-stage product work, where metrics do not yet exist and evaluation methods have to be inventedYou want to shape products, not just measure them, and are comfortable speaking up when the data disagrees with the planExperience evaluating machine learning or AI-driven systems in production, including offline evaluation, online experimentation, and ongoing performance monitoringExperience in ad-tech, marketplace, or e-commerce environments (specifically bidding, ranking, or auction logic)Familiarity with causal inference methods or evaluation approaches for products where standard A/B testing does not applyExperience working with geographically distributed teams and stakeholders