Marketing Analytics & Predictive Modeling Specialist

SQL, Python, Pandas, NumPy, Scikit-learn, Data cleaning, Data wrangling, Data manipulation, Jupyter Notebooks, MySQL, PostgreSQL, Data analysis, Business analysis, Exploratory data analysis, Descriptive analytics, Predictive modeling, Forecasting, KPI tracking, Dashboard development, Power BI, Tableau, Excel VBA, Advanced Excel, Hypothesis testing, Regression analysis, A/B testing, Statistical modeling, Time series analysis, Probability, Statistics, SAP, SAS, Git, JIRA, Azure, Cost optimization, Pricing strategy, Profitability analysis, Financial modeling, Strategic decision support, Stakeholder management, Cross -functional collaboration, Process improvement, Supply chain analytics, Problem-solving, Attention to detail, Communication, Analytical thinking, Initiative, Leadership, Project ownership ABHISHEK JHA

Seattle, Washington ********.*******@*******.*** +1-206-***-**** Linkedin Portfolio EXECUTIVE SUMMARY

Marketing and Customer Analytics Professional with 3.5+ years of experience blending SQL, Python and statistical modeling to drive customer acquisition, retention and monetization strategies. Deep expertise across the analytics lifecycle: from building multi-channel attribution and Marketing Mix Models (MMM) to deploying churn propensity models and optimizing customer Lifetime Value (LTV:CAC). Proven track record leading customer segmentation, designing quasi-experiments and translating complex data into executive BI dashboards. Successfully reallocated 15% of underperforming ad budgets, reduced customer churn by 8% and delivered a $520K annualized profit lift via targeted promotional experiments.

EDUCATION

University of Washington – Michael G. Foster School of Business Seattle, WA Master of Science, Business Analytics June 2025 – June 2026 Guru Gobind Singh Indraprastha University Delhi, India Bachelor of Technology, Mechanical Engineering July 2015 – June 2019 PROFESSIONAL EXPERIENCE

Gabriel Business Analyst (Marketing & Business Development) Dec 2022 – Jan 2024

• Customer Value & Retention: Built customer churn propensity models (XGBoost, Logistic Regression) using behavioral data, prioritized at-risk B2B accounts for targeted save plays, reducing segment churn by 8%.

• LTV Optimization: Developed cohort-based LTV models to size customer accounts and guide retention investment, improving the LTV:CAC efficiency ratio by 12% across targeted programs.

• Experimentation & Promos: Designed and evaluated A/B and pilot promotional frameworks using invoice natural experiments, led an 80-SKU pricing pilot that delivered a 6–8% profit lift ($520K annualized) with built-in sales rollback triggers.

• Marketing Driver Models: Built regression-based media mix driver models linking trade promotions, pricing moves and economic proxies to revenue, recommended an 8% trade spend reallocation away from "always-on" discounts toward incremental high-lift segments.

• Journey Mapping: Implemented multi-touch customer journey views (First Order Repeat Churn Risk) to move stakeholders away from single-touch attribution, aligning promotional schedules with the sales pipeline. Aisin Data Analyst (Marketing & Sales) Jan 2020 – Dec 2022 Segmentation and targeting

• Customer Segmentation: Engineered RFM (Recency, Frequency, Monetary) clustering models to segment accounts into high-value, price- sensitive and at-risk cohorts, enabling personalized promotional targeting.

• Campaign Analytics: Evaluated recurring promotional calendar performance using hypothesis testing and regression, isolated margin- draining campaigns to improve recurring promotional ROI.

• Funnel Tracking: Tracked lifecycle health metrics including ARPU, ASP, account reorder rates and quote-to-order funnel conversion layers, supporting portfolio actions that lifted marginal profit by 4%.

• BI Automation: Built automated Python/SQL data pipelines linking ERP transaction data directly to Power BI dashboards, speeding up executive leadership lifecycle reporting by 20%.

GRADUATE PROJECTS (UW MSBA) Live: Analytics Systems Portfolio

• Microsoft – Cross-Portfolio Customer Lifetime Value (CLV) Optimization: Engineered a unified CLV framework across multi-category product lines (M365, Xbox, Surface) using hierarchical Bayesian models to optimize cross-sell allocation and retention spend.

• DTC Clothing Brand – Channel Spend & Incrementality Capstone: Analyzed 18 months of marketing data via regression to reallocate 15% of budget away from negative-ROI channels, driving a mid-single-digit sales uptick.

• Pet Products Chain – Retention & Promo: Built ML based automated weekly targeting, cutting $2K–$3K/quarter in promotional spend.

• Pet Products Chain – Seattle Geographic Expansion: Developed a geographic demand framework using municipal pet registration data and competitor location gaps to support a 10-store retail expansion plan. TECHNICAL SKILLS

Marketing science: Marketing mix modeling (MMM), multi-touch attribution, A/B and quasi-experiments, CUPED, uplift modeling, elasticity, promo effectiveness, incrementality concepts, Churn, LTV, CAC, cohort analysis, RFM, segmentation, funnel analysis Modeling: Regression, time-series forecasting, predictive modeling (scikit-learn, XGBoost), hypothesis testing Languages: Python (pandas, NumPy, scikit-learn), SQL (Snowflake, Redshift, PostgreSQL, MySQL), R Data & BI: ETL, data quality, Power BI, Tableau, Excel, AWS, Azure, Git, Jupyter, AI-assisted analysis (LLM APIs, notebooks), dbt, Looker

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