Amol Milind Paradkar

• Seasoned Data Scientist specializing in Supply Chain Analytics, with extensive experience leading teams and projects. Adept at leveraging advanced analytics, machine learning, and operations research methodologies to develop scalable, deployable models that drive business value. Strong expertise in supply chain planning, inventory optimization, and data-driven decision-making.
  
  Key Responsibilities & Expertise
  Leading and managing multiple projects, from inception to deployment, ensuring successful delivery and alignment with business objectives.
  
  Applying advanced analytics techniques, including regression , classification, and optimization models, to enhance supply chain efficiency.
  
  Designing and implementing dashboards in Tableau for data visualization and stakeholder communication.
  
  Conducting stakeholder presentations and knowledge-sharing sessions within the organization.
  
  Skills & Technologies
  Machine Learning & AI: Supervised learning, Unsupervised learning, Optimization, Monte Carlo Simulation, Clustering Generative AI, Retrieval-Augmented Generation (RAG),
  
  Supply Chain & Operations Research: Supply Chain Planning, Inventory Management, Demand Forecasting, Multi-Echelon Inventory Optimization (MEIO)
  
  Programming & Tools: Python, SQL, R, GCP, Azure, Databricks, C3.ai, Tableau
  
  Key Achievements
  Led multiple client projects across high-tech and retail industries, overseeing solutions from ideation to implementation and enhancement.
  
  Developing end-to-end machine learning solutions, encompassing requirement gathering, MVP development, exploratory data analysis (EDA), data modeling, pipeline development, model evaluation, and deployment.

Developed a predictive model using regression (XGBoost) and classification techniques to assess supply commitments from partners, improving forecast accuracy.

Designed and implemented an optimization model for inventory allocation, maximizing revenue and optimizing stock distribution.

Built an AI-driven Supply Planner Visibility Tool, on the above project integrating LLMs within the ACN environment to enhance supply chain transparency.

Developed a retail supply chain optimization engine using linear programming and regression, balancing delivery speed and cost to prevent inventory misallocation.

Applied Monte Carlo simulation and clustering techniques to develop a Multi-Echelon Inventory Optimization (MEIO) model, enabling precise inventory placement across distribution centers (DCs) and manufacturing facilities (MFGs).

Created an inventory forecasting model based on a modified MRP approach to predict quarterly inventory levels for raw materials and finished goods.

Actively contributed to the Supply Chain Analytics Practice, leading business development initiatives, asset creation, and internal knowledge-sharing efforts.

• Collaborated with cross-functional teams to successfully deliver comprehensive solutions for clients.
• Managed client relationships through regular check-ins and updates on project progress.
• Evaluated client needs and expectations, establishing clear goals for each consulting engagement.
• Developed innovative strategies for clients, resulting in increased revenue and business growth.

• Creating acquisition scorecards for Housing loans and farm equipment loans using logistic regression, XgBoost
• Automation and deploying of the acquisition scorecard using GCP and python
• Built an estimation model to determine the missing salary of the home loan customers
• Building cross-sell and up-sell models to identify customers who are likely to buy topup loans and PL using classification and clustering
• Reduced time spent on manual tasks by developing automated processes using Python scripts and SQL queries.
• Creating monitoring scripts to perform diagnostics on the health of the models
• Develop pipelines using Pub/Sub, Dataflow, BiqQuery and Python
• Present information using excel, Tableau and PowerPoint
• Enhanced business decision-making with robust statistical analyses, including regression modeling and hypothesis testing.
• Developed custom data pipelines for efficient data processing, leading to improved model performance.

• Apply various machine learning algorithms to solve complex financial problems on clustering, Dormancy, customer churn, sales forecasting, up-sell /cross sell, fraud detection, customer segmentation, product branding and pricing
• Apply analytical rigor on huge datasets which should help in understanding the customer behaviour and identify revenue opportunities
• Analysis would include Hypothesis Testing, Campaign Management and Ad hoc Analytics
• Roll out dashboards/MIS as per need and work with different departments for ad- hoc MIS and analysis requests

• SME for File Transfer, jitterbit, a commercial loan application(CLOE), Accurate
• Used Python and SQL to scrape, clean, and analyse large datasets
• Automation and enhancement of various jobs and programs in VbScript, R, Python