{"id":200991,"url":"https://alion.io/job/target-sr-data-scientist-space-presentation","title":"Sr Data Scientist- Space Presentation","company":{"id":3684,"name":"Target","domain":"target.com","url":"https://alion.io/company/target","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":95,"open_postings":322,"ghost_share":0.022,"stale_share":0.311,"repost_share":0.04,"time_to_fill_p50_days":8,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","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":11500,"max_usd":25000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Machine Learning","optional":false},{"name":"RAG","optional":false},{"name":"A/B Testing","optional":true},{"name":"Docker","optional":true},{"name":"Embeddings","optional":true},{"name":"Function Calling","optional":true},{"name":"GCP","optional":true},{"name":"Hadoop","optional":true},{"name":"Human-in-the-Loop","optional":true},{"name":"Interpretability","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LLM","optional":true},{"name":"MLFlow","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"pySpark","optional":true},{"name":"Python","optional":true},{"name":"Spark","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-09-04T12:55:18Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-10-29T17:07:45Z","board_verified":true,"closed_at":null,"days_open":55,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":55},"description":"About Us\nAs a Fortune 50 company with more than 400,000 team members worldwide, Target is an iconic brand and one of America's leading retailers.\nJoining Target means promoting a culture of mutual care and respect and striving to make the most meaningful and positive impact. Becoming a Target team member means joining a community that values different voices and lifts each other up. Here, we believe your unique perspective is important, and you'll build relationships by being authentic and respectful.\nOverview about TII\nAt Target, we have a timeless purpose and a proven strategy. And that hasn’t happened by accident. Some of the best minds from different backgrounds come together at Target to redefine retail in an inclusive learning environment that values people and delivers world-class outcomes. That winning formula is especially apparent in Bengaluru, where Target in India operates as a fully integrated part of Target’s global team and has more than 5000+ team members supporting the company’s global strategy and operations.\nPyramid Overview\nA role with Target Data Science & Engineering means the chance to help develop and manage state of the art predictive algorithms that use data at scale to automate and optimize decisions at scale. Whether you join our Statistics, Optimization or Machine Learning teams, you’ll be challenged to harness Target’s impressive data breadth to build the algorithms that power solutions our partners in Marketing, Supply Chain Optimization, Network Security and Personalization rely on.\nTeam Overview\nThe Space/Presentations Data Science team builds data science capabilities that help Target make better Planogram decisions across stores. The team develops ML and Optimization models and decisioning systems that estimate Sales, understand space elasticity, optimize Planogram fitment, measure incrementality, and support POG execution strategies that balance sales, margin, guest value, competitive position, and business guardrails.\nPlanogram is a critical lever for how guests interact with Target at stores ,spurs sales and makes enterprise growth, affordability, guest trust, and profitability. The team works at the intersection of machine learning, econometrics, forecasting, optimization, experimentation, retail science, and production decisioning to improve how prices are recommended, reviewed, measured, and scaled across categories.\nRole Overview\nAs a Senior Data Scientist in Merchandising , you will help build and improve data science ML and Optimization models that power Target's Planogram capabilities. The primary focus of this role will be Sales Forecasting and elasticity models with optimization-based presentation recommendations.\nYou will partner with Data Scientists, Product Managers, Engineers, Analysts, Merchandising partners, and business stakeholders to translate complex problems into scalable modelling solutions.\nThis role is ideal for someone with strong foundations in machine learning, statistical modeling, forecasting, and applied optimization, with interest in solving high-impact retail problems at scale. Experience with Generative AI, LLMs, RAG, or AI agents is a plus as the team explores AI-enabled measurement, explainability, monitoring, and decision-support workflows.\nKey Responsibilities\nDevelop, validate, and improve forecasting and elasticity models (using Regressions) that estimate Sales which is used as input for facings recommendations on Planogram.\n\nAccount for multiple variables present in forecasting and separate impact of target variable on Sales.(Vif, multicollinearity)\n\nUse optimization to recommend optimal item placements on POG such that expense to service POG’s is lower and all item facings which are recommended fit on the POG (constrained Linear programming including the use of Fuzzy logic constraints)\n\nCreate Item groups/segments to measure POG Performance and recommend changes using segmentation and similarity measures\n\nScale and deploy solution to production environments\n\nCreate measurement frameworks to evaluate model performance\n\nPartner with business and product teams to understand strategy, define success metrics, and translate requirements into model design.\n\nWork with large-scale retail data including sales, presentation history, item attributes, inventory, store and market attributes, and guest demand signals.\n\nConduct deep-dive analyses to diagnose model performance, elasticity behavior, underperforming recommendations, outliers, sparse data, and category-specific pricing patterns.\n\nSupport experimentation and measurement design, including A/B tests, market tests, incrementality measurement, control/test methodology, and model impact assessment.\n\nCollaborate with ML Engineers and Software Engineers to productionize models, automate pipelines, improve reliability, and integrate outputs into business-facing workflows.\n\nMonitor model performance over time, identify drift or degradation, and recommend improvements to maintain model quality and business impact.\n\nCommunicate model logic, assumptions, trade-offs, risks, and recommendations clearly to technical and non-technical stakeholders.\n\nContribute to model explainability and adoption by helping business partners understand why recommendations are generated.\n\nExplore GenAI, LLMs, RAG, and agents for pricing use cases such as explainability, measurement automation, performance monitoring, and recommendation efficiency.\n\nAbout You\nBachelor’s, Master’s, or PhD in Data Science, Statistics, Economics, Mathematics, Operations Research, Computer Science, Engineering, or a related quantitative field.\n\n4+ years of relevant experience in data science, applied machine learning, , forecasting, optimization, retail domain knowledge, GCP , Big Data.\n\nStrong hands-on experience building and validating machine learning or statistical models in a business setting.\n\nExperience within Merchandising on elasticity modeling, demand modeling and forecasting.\n\nStrong understanding of statistical concepts, model evaluation, feature engineering, regularization, cross-validation, uncertainty, and model interpretability.\n\nExperience with Optimization such as constrained optimization, linear programming, mixed-integer programming,\n\nExperience with experimentation and measurement.\n\nAbility to work on Big Data\n\nAbility to scale solutions to production enviironments\n\nStrong programming skills in Python and SQL, with experience working on large datasets using Spark, PySpark, Hive, Hadoop, or similar platforms.\n\nAbility to analyze complex data, diagnose model issues, and convert findings into actionable recommendations.\n\nAbility to work in ambiguous problem spaces, structure analytical approaches, and deliver high-quality outcomes against business timelines.\n\nStrong communication and collaboration skills, with the ability to partner across Data Science, Product, Engineering, Analytics, Merchandising, and business teams.\n\nMust-Have Skills\nStrong experience in Python, SQL, and large-scale data analysis.\n\nHands-on experience with machine learning, statistical modelling, and model validation.\n\nExperience with demand forecasting, elasticity modelling and optimization.\n\nStrong understanding of feature engineering, backtesting, model evaluation, and performance diagnostics.\n\nExperience working with large-scale structured data using Spark, PySpark, Hive, Hadoop, or similar platforms.\n\nBasic to intermediate experience with optimization methods, simulations, or constraint-based decisioning.\n\nAbility to translate business problems into analytical and modeling solutions.\n\nStrong documentation, storytelling, and stakeholder communication skills.\n\nPreferred / Good-to-Have Skills\nExperience in retail, merchandising.\n\nExperience with scalable model pipelines, automated retraining, model monitoring, explainability, and MLOps practices.\n\nExperience with market testing, synthetic controls, double-delta measurement, or causal impact frameworks.\n\nExposure to Generative AI and LLM applications, including prompt engineering, RAG, embeddings, vector databases, evaluation, and workflow automation.\n\nExposure to agentic AI systems, including AI agents, tool use, LangGraph, LangChain, LlamaIndex, and human-in-the-loop workflows.\n\nExperience building explainability, monitoring, or decision-support tools for business users.\n\nExperience with cloud platforms, APIs, containerization, workflow orchestration, MLflow, Airflow, Docker, Kubernetes, or similar tools.\n\nKnow More About Us here:\nLife at Target - https://india.target.com/\n\nBenefits - https://india.target.com/life-at-target/workplace/benefits\n\nCulture- 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