{"id":1653073,"url":"https://alion.io/job/valerie-group-data-scientist","title":"Data Scientist","company":{"id":685122,"name":"Valerie Group","domain":"valeriegroup.com","url":"https://alion.io/company/valeriegroup","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":47000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":55},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"BigQuery","optional":true},{"name":"dbt","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Machine Learning","optional":true},{"name":"Metabase","optional":true},{"name":"NumPy","optional":true},{"name":"Power BI","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Snowflake","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-10-01T11:25:25Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-13T09:36:43Z","board_verified":true,"closed_at":null,"days_open":11,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":11},"description":"About the Role\nWe 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.\nIn 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. You will 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.\nKey Accountabilities\n1. Product Analytics & Insights\nDefine and track key product metrics, including user behavior, funnels, drop-offs, and feature performance.\n\nTranslate complex product questions into structured analysis and actionable recommendations that directly influence product, growth, commercial, or investment decisions.\n\n2. Applied Data Science & Modeling\nBuild and apply models for segmentation, scoring systems, classification, and recommendations, ensuring alignment with product use cases and practical deployment.\n\nPartner with Engineering and Data teams to operationalize models and analytical workflows to ensure production-grade reliability.\n\nWork effectively with incomplete, imperfect, or banded data-making assumptions explicit and identifying gaps that materially affect analysis or decision-making.\n\nOwn analytical and modeling projects end-to-end: from defining the business problem and data requirements through modeling, validation, deployment, and impact measurement.\n\nBuild and apply forecasting, unit economics, scoring, incrementality, media effectiveness, or other decision models relevant to commercial and product use cases.\n\n3. Experimentation & Testing\nDesign and run experiments, including A/B testing and cohort analysis.\n\nMeasure the impact of product changes, AI outputs, and workflow adjustments.\n\nBuild feedback loops to continuously improve product and model performance over time.\n\nQuantify uncertainty, test assumptions, and clearly communicate the limitations and confidence levels of analytical conclusions.\n\n4. Data Infrastructure & Quality\nDefine data requirements for new product features and ensure data tracking is accurate, consistent, and complete.\n\nCollaborate with engineering to structure datasets and pipelines for analysis and modeling.\n\nIdentify and resolve gaps in data visibility.\n\nIdeal Background & Qualifications\nExperience: 6-8 years of relevant analytics or data science experience, with demonstrated ownership of meaningful analytical or modeling work.\n\nProblem-Solving: Strong ability to translate ambiguous business, product, growth, or commercial problems into structured data questions and actionable analytical approaches.\n\nDomain Knowledge: Experience working with product metrics, funnels, and user behavior analysis. Experience in growth, DTC/e-commerce, marketplace, subscription, ad-tech, agency, or unit-economics-driven environments is preferred.\n\nModeling Expertise: 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.\n\nCommercial Acumen: Strong understanding of growth and commercial metrics (CAC, AOV, COGS, contribution margin, payback period, breakeven) to inform business decisions.\n\nTechnical Stack:\nStrong SQL skills.\n\nExperience with structured and semi-structured data.\n\nStrong working proficiency in Python for data analysis, modeling, experimentation, and automation (R experience is acceptable if backed by equivalent analytics capability).\n\nProficiency with libraries such as pandas, NumPy, and scikit-learn.\n\nExperimentation & AI: 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).\n\nNice-to-Haves:\nExposure to data visualization tools (Tableau, Power BI, Metabase).\n\nExperience in zero-to-one startup environments.\n\nExperience with media mix modeling, incrementality, causal inference, experimental design, or media effectiveness measurement.\n\nExperience with modern cloud data warehouses/workflows (Snowflake, BigQuery, dbt) and attribution platforms (Triple Whale, Northbeam, Rockerbox).\n\nExperience applying machine learning models in production.\n\nWhat Success Looks Like (KPIs)\nAI & Integration: AI outputs are reliably interpreted and integrated into product workflows.\n\nData-Driven Decisions: Product decisions are consistently backed by clear, actionable data insights.\n\nOperationalization: Analytical and models are successfully deployed and actively used in product, growth, or commercial workflows.\n\nMeasurable Impact: Work delivers measurable improvements in product performance, growth, commercial outcomes, or decision quality.\n\nExperimentation: Experiments lead to measurable enhancements in product performance.\n\nData Robustness: Data tracking and pipelines are robust, and quality gaps are identified and addressed early.\n\nCross-Functional Alignment: Product, Engineering, and Data teams maintain clear alignment on data requirements, model outputs, and operationalisation.\n\nValues\nWe take our values very seriously and ask that all team members reference (or keep in mind) values in dealing with conflict, challenges, opportunities and day-to-day operations.\nEach individual's role is to also hold others accountable and themselves.\nAccountability - own your part in it first.\n\nCollaboration - take initiative with discipline and humility.\n\nResilience - to be steadfast, assess, and bounce back.\n\nSelflessness - ‘what does it take to scale’.\n\nFun - be present and connect.\n\nThe best time to join Valerie was yesterday. The next best time is now \nOur Hiring Process\nWe want interviews to be valuable for both sides. Throughout the process, you'll meet the people you'll work with, learn more about the role, and get a chance to understand how we think and operate.\nOur process typically includes:\nIntroductory conversation with our Talent team 30 mins\n\nHiring Manager interview 30-45 mins\n\nRole-specific assessment or practical exercise (where applicable)\n\nCross-functional or stakeholder interviews 30-45 mins\n\nFinal conversation with leadership 30-45 mins\n\nWe'll always let you know what to expect before each stage.\nWhat You Can Expect\nMeaningful work with visible impact\n\nHigh ownership from day one\n\nCollaboration with experienced founders, operators, and specialists\n\nA team that values curiosity, initiative, and continuous improvement\n\nCompetitive compensation and the tools you need to do your best work\n\nOpportunities to grow as Valerie grows\n\nValerie is committed to building an inclusive workplace where people from different backgrounds, experiences, and perspectives can thrive. 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