Greater Delhi Area
Let's connect: https://topmate.io/abhishekchoubey0109/ Data Scientist | Machine Learning Expert | Gold Medalist in Operational Research | Python | SQL | Deep Learning | Computer Vision | Predictive Modeling | Analytics Strategy I am a results-driven and innovation-focused Data Scientist with 3+ years of experience delivering high-impact machine learning and analytics solutions across leading organizations including Blinkit, Axtria, and NITI Aayog. With a solid academic foundation — Gold Medalist in Operational Research from Hindu College (Tier 1) and a Master’s degree in Operational Research — I bring both theoretical depth and practical excellence to every project. 🚀 Key Strengths: Advanced proficiency in Python, SQL, and PyTorch, with deep expertise in building, deploying, and scaling machine learning and deep learning models. Hands-on experience in supervised and unsupervised learning, regression and classification models, neural networks, and NLP. Specialized in computer vision and image-based modeling, with successful applications in object detection, segmentation, and model optimization. Strong grasp of data engineering, feature engineering, model evaluation, and business intelligence tools to drive actionable insights. Proven track record of using data-driven decision making to improve operational efficiency and drive revenue growth. 🔍 Core Competencies: Machine Learning · Deep Learning · Artificial Intelligence · Computer Vision · NLP · Time Series Forecasting · Model Deployment · Data Science Strategy · Predictive Analytics · PyTorch · TensorFlow · SQL · Python · Scikit-learn · Pandas · NumPy · Data Visualization · Experimentation Design · Statistical Analysis · Business Intelligence · Big Data · Image Classification · A/B Testing 💡 I thrive in solving complex business problems with elegant data solutions, continuously exploring cutting-edge algorithms and tools to stay ahead in the fast-evolving field of AI and Data Science. Let’s connect if you're interested in data-driven innovation, AI-first strategies, or collaboration on analytics-led transformation.
Built production-grade ML systems that improve inventory planning, automate operational workflows, enhance warehouse visibility, and enable data-driven decision making across quick commerce operations. 🔑 Areas of Impact • Demand forecasting and inventory optimization using statistical and machine learning models. • Warehouse and Store automation through computer vision, depth estimation, and Vision-Language Models (VLMs). • Product taxonomy and catalog intelligence through clustering and NLP techniques. • Experimentation, causal inference, and personalization to drive product and business decisions. • Operational analytics and workflow automation across fulfillment and supply chain processes. 🚀 Selected Contributions • Developed adaptive forecasting systems that improved inventory planning and product availability across categories. • Built an autonomous auditing platform using robotics, computer vision, depth maps, and VLMs to automate audits at stores and warehouses. • Designed edge-AI solutions for store monitoring using custom computer vision models deployed on low-cost hardware. • Improved catalog quality and product classification through unsupervised learning and semantic modeling. Passionate about building AI systems that bridge the gap between advanced machine learning and real-world operational impact.
Applied machine learning, statistical modeling, experimentation, and commercial analytics to solve complex business problems across pharmaceutical and healthcare domains. Partnered with commercial, marketing, and consulting teams to develop data-driven solutions for customer targeting, promotional optimization, physician engagement, patient analytics, and business strategy. 🔑 Areas of Expertise • Recommendation systems, personalization, and Next Best Action frameworks. • Predictive modeling for physician targeting, prescribing behavior, and customer acquisition. • Experimentation, causal inference, and marketing effectiveness measurement. • Financial and operational analytics for business performance optimization. • Commercial strategy, account prioritization, and sales intelligence. • Machine learning model development using ensemble methods, collaborative filtering, and propensity modeling. 🚀 Selected Contributions • Built Next Best Action and personalized recommendation engines to optimize promotional strategies and customer engagement. • Developed advanced targeting and propensity models to identify high-potential accounts and improve outreach effectiveness. • Designed recommendation systems using collaborative filtering, ensemble learning, and contextual decisioning approaches. • Leveraged machine learning to predict physician prescribing behavior and support data-driven commercial planning. • Built conversion propensity frameworks to prioritize physicians and accounts most likely to adopt targeted products. • Conducted financial and operational analytics to identify optimization opportunities and support strategic business decisions. • Performed contracting and payer analytics to evaluate business performance and recommend structural improvements.
Worked on data analytics, machine learning, and AI evaluation initiatives supporting healthcare and public-sector decision-making. 🔑 Areas of Contribution • Machine learning and computer vision for healthcare applications. • Statistical analysis, model evaluation, and AI solution benchmarking. • Research, reporting, and stakeholder-facing analytics. • Data management and evidence-based policy support. 🚀 Selected Contributions • Developed a machine-learning-based screening framework for diabetic retinopathy using retinal image data, enabling automated risk identification and reducing dependence on manual review. • Conducted comparative evaluations of AI solutions using statistical analysis, experimentation frameworks, and predictive modeling to assess performance, scalability, and deployment readiness. • Produced analytical reports and recommendations to support technology adoption and decision-making across healthcare-focused initiatives. • Streamlined data analysis and reporting workflows, improving turnaround time and operational efficiency for stakeholder deliverables. • Collaborated with cross-functional teams to translate analytical findings into actionable insights for policy and program evaluation.