Posted today · be early
Data Scientist
Valerie Group
IndiaremotePosted today
Skill Required
Data-ScientistData-Science-ExpertData-ScienceData-Science-SpecialistData ScientistMachine LearningScikit-learnStatisticsEngineeringanalyticalautomationSnowflakePower BIanalyticsBigQuerybuildingTableauTestNGPythonPandasdesignNumPyCloudUnitydbtSQLandGenerative AIAIFulltime
Key highlights
- 6–8 years of relevant experience
- Competitive compensation
- Strong SQL and Python proficiency required
- Focus on growth, commercial, and product analytics
Role overview
We are hiring a Data Analyst / Data Scientist to drive growth, commercial and product analytics, applied data science, and AI-driven capabilities across Valerie's Brands and Investment Growth Platform. In this role, you will work across product analytics, modeling, experimentation, and data workflows, using SQL and Python to turn complex business and product questions into actionable insights and production-ready analytical solutions.
Responsibilities
- Define and track key product metrics, including user behavior, funnels, drop-offs, and feature performance.
- Translate complex product questions into structured analysis and actionable recommendations that directly influence product, growth, commercial, or investment decisions.
- Build and apply models for segmentation, scoring systems, classification, and recommendations, ensuring alignment with product use cases and practical deployment.
- Partner with Engineering and Data teams to operationalize models and analytical workflows to ensure production-grade reliability.
- Work effectively with incomplete, imperfect, or banded data—making assumptions explicit and identifying gaps that materially affect analysis or decision-making.
- Own analytical and modeling projects end-to-end: from defining the business problem and data requirements through modeling, validation, deployment, and impact measurement.
- Build and apply forecasting, unit economics, scoring, incrementality, media effectiveness, or other decision models relevant to commercial and product use cases.
- Design and run experiments, including A/B testing and cohort analysis.
- Measure the impact of product changes, AI outputs, and workflow adjustments.
- Build feedback loops to continuously improve product and model performance over time.
- Quantify uncertainty, test assumptions, and clearly communicate the limitations and confidence levels of analytical conclusions.
- Define data requirements for new product features and ensure data tracking is accurate, consistent, and complete.
- Collaborate with engineering to structure datasets and pipelines for analysis and modeling.
- Identify and resolve gaps in data visibility.
- Collaborate closely with Product, Engineering, and Data teams to operationalize models, shape how AI and model outputs are integrated into the product, validate model-driven decisions, and build data systems that continuously enhance product and commercial performance.
Requirements
- 6–8 years of relevant analytics or data science experience, with demonstrated ownership of meaningful analytical or modeling work.
- Strong ability to translate ambiguous business, product, growth, or commercial problems into structured data questions and actionable analytical approaches.
- Experience working with product metrics, funnels, and user behavior analysis.
- Experience building and applying classification, regression, clustering, scoring, forecasting, and decision systems, with evidence of taking at least one meaningful model or analytical product into real business use.
- Strong understanding of growth and commercial metrics (CAC, AOV, COGS, contribution margin, payback period, breakeven) to inform business decisions.
- Strong SQL skills.
- Experience with structured and semi-structured data.
- Strong working proficiency in Python for data analysis, modeling, experimentation, and automation (R experience is acceptable if backed by equivalent analytics capability).
- Proficiency with libraries such as pandas, NumPy, and scikit-learn.
- Experience with A/B testing, statistical analysis, measurement, causality, and impact evaluation.
- Experience evaluating LLM/AI-generated outputs in product workflows (understanding response structures, quality, consistency, and reliability).
Nice to have
- Experience in growth, DTC/e-commerce, marketplace, subscription, ad-tech, agency, or unit-economics-driven environments.
- Exposure to data visualization tools (Tableau, Power BI, Metabase).
- Experience in zero-to-one startup environments.
- Experience with media mix modeling, incrementality, causal inference, experimental design, or media effectiveness measurement.
- Experience with modern cloud data warehouses/workflows (Snowflake, BigQuery, dbt) and attribution platforms (Triple Whale, Northbeam, Rockerbox).
- Experience applying machine learning models in production.
Benefits
- Meaningful work with visible impact
- High ownership from day one
- Collaboration with experienced founders, operators, and specialists
- A team that values curiosity, initiative, and continuous improvement
- Competitive compensation and the tools you need to do your best work
- Opportunities to grow as Valerie grows
Additional details
- KPIs: AI outputs are reliably interpreted and integrated into product workflows.
- KPIs: Product decisions are consistently backed by clear, actionable data insights.
- KPIs: Analytical and models are successfully deployed and actively used in product, growth, or commercial workflows.
- KPIs: Work delivers measurable improvements in product performance, growth, commercial outcomes, or decision quality.
- KPIs: Experiments lead to measurable enhancements in product performance.
- KPIs: Data tracking and pipelines are robust, and quality gaps are identified and addressed early.
- KPIs: Product, Engineering, and Data teams maintain clear alignment on data requirements, model outputs, and operationalisation.
- Values: Accountability- own your part in it first.
- Values: Collaboration- take initiative with discipline and humility.
- Values: Resilience- to be steadfast, assess, and bounce back.
- Values: Selflessness- ‘what does it take to scale’.
- Values: Fun- be present and connect.
- Hiring Process: Introductory conversation with our Talent team <> 30 mins
- Hiring Process: Hiring Manager interview <> 30-45 mins
- Hiring Process: Role-specific assessment or practical exercise (where applicable)
- Hiring Process: Cross-functional or stakeholder interviews <> 30-45 mins
- Hiring Process: Final conversation with leadership <> 30-45 mins
- Valerie is committed to building an inclusive workplace where people from different backgrounds, experiences, and perspectives can thrive. We hire based on skills, potential, and the ability to contribute to our team.
- Originally posted on Himalayas
