Skills
Jupyter/Python, R 100%I am a strategic leader in business insights, knowledge-driven and decision support systems and AI-related applications, with 20+ years of experience in leveraging AI, data, and predictive analytics to drive business growth across consumer-driven industries. Passionate about transforming complex data into actionable strategies, I specialize in:
With a track record of delivering measurable and customer-centric business impact, I have built AI-enabled customer segmentation models, decision support systems, personalized recommendation systems, churn and demand prediction models, and optimized digital communication and business strategies to improve revenue growth, customer engagement, and operational efficiency. I thrive in fast-paced, data-rich environments, where AI and data can drive intelligent decision-making for business transformation and continuous adaptation to current market conditions.
Passionate about delivering busines services with significant impact on business transformation, business adaptation or bysiness strategy.
Design, implementation, testing, deployment and maintenance of personal AI Assistants, chatbots, multi-agent workflows and task automations. Implementation, model training and performance tuning for Recommender Systems, Ranking Systems (learning-to-rank), semantic search and fuzzy matching/mapping.
Feature extraction, model training, deployment, tuning, performance evaluation and integration of Classic/Traditional oMachine Learning Models (using ML methods and techniques like linear/logistic regression, neural networks, deep learning, ensemble learning (bagging, boosting, stacking), text/image classification, forecasting models, anomaly/outlier detection, customer segmentation, churn prediction, etc. Development & Programming languages employ JupyterLab/JupyterHub, Anaconda, Python, R or SQL, and one of the ML Frameworks like PyTorch, TensorFlow, Scikit-learn, XGBoost, HuggingFace, etc.
Dataset preparation, model training, tuning, evaluation and integration of service components for content generation using Generative AI and multi-modal conversation using LLMs (Transformers, self-attention, positional embeddings, fine-tuning, Retrieval-Augmented Generation (RAG), vector search (FAISS), LLMs (GPT, Llama, Gemini, DeepSeek), tool-calling agents, prompt engineering, llama.cpp), LLM Apps (LangChain, LlamaIndex, AutoGen, Ollama, llama.cpp, vLLM, LM Studio, Open Web UI), agentic workflows (Copilot, LangGraph, CrewAI, Claude).
NLP Document services: Web & document scraping, semantic search & indexing, data discovery, NER/Namedy Entity Recognition, Q&A/Question Answering generation, Natural Language Processing & Representation Learning (word embeddings (Word2Vec, GLOVE), contextual embeddings (BERT), information retrieval, text mining). Data validation, mapping, structuring, disambiguation, Entity recognition/matching, missing data imputation, data visualization, dataset versioning, formatting & serving/dissemination, data drift monitoring, data usage monitoring.
Computer Vision tasks, model training, testing, integration and performance measurement: OpenCV, object detection, ResNet, YOLO, image recognition, OCR/character recognition, Diffusion Models, Multi-modal LLM models (PaliGemma), image segmentation (SAM/Segment Anything, DINO, Detectron, SAMURAI). Visual object feature extraction, annotation (auto-tagging, classification), product matching/recognition based on visual embeddings. Similar product ranking, generation.
Statistics Services: Descriptive Statistics, Inferential Statistics, Bayesian A/B Testing, Causal Inference
Optimization Methods (Linear & Mixed Integer Programming, Genetic Algorithms, Bayesian Optimization), Reinforcement Learning (model-free RL, Q-learning, Soft Actor-Critic, DDPO/Deep Deterministic Policy Gradient, regret-minimization methods (Multi-armed Bandit, Upper Confidence Bound))
$MLOps (Docker, Kubernetes, CI/CD pipelines (Git, Gitlab), MLflow experiment tracking, model drift & performance monitoring (Evidently, Grafana)). Cloud & AI Infrastructure (AWS/Amazon Web Services, Microsoft Azure, GCP/Google Cloud Platform, Databricks, Hugging Face). Big Data Ingestion & Querying (Airflow, BigQuery, AWS Athena, Spark, Databricks). Distributed Model Training (DeepSpeed, Ray, CUDA) and ML Model Serving (TensorFlow Serving, TorchServe, Seldon).
PROJECTS COMPLETED
YEARS OF EXPERIENCE
TOTAL CLIENTS
USE CASES/SOLUTIONS DEFINED
Marketing for the Retail Domain.
Industry:Retail
Problem: Suggest relevant products to maximize engagement/sales.
Method: Factor Analysis (PCA/SVD), Latent Dirichlet Allocation, Matrix Factorization.
Key Metrics: Precision@K, Recall@K, Click-Through Rate (CTR).
Solution: Embed near real-time recommendations in "Recommended for You" widgets.
Approach: Employ Low-Rank Matrix Approximation and User-Item Latent Factors, and use Singular Value Decomposition on Interaction Matrix
Industry:Retail
Problem: Identify customers likely to stop buying from our brand.
Method: Survival Analysis (Cox PH), Regularization (Lasso/Logistic).
Key Metrics: Hazard Rate, Retention Probability at t+12 months.
Solution: Trigger proactive churn intervention offers.
Approach: Calculate hazard rates & survival curves. Apply feature selection via ElasticNet and Time-to-event data analysis
Industry:Retail
Problem: Group customers into meaningful clusters for targeted messaging.
Method: Word2Vec embeddings, Clustering (K-Means), PCA/Dimensionality Reduction, t-SNE Visualization.
Key Metrics: Cluster Cohesion, Segment Size Distribution.
Solution: Persona-based email marketing flows.
Approach: Construct a representation of customer behaviour using Word2Vec embeddings. Apply Elbow Method / Silhouette Score Validation; Dimensionality reduction before clustering. Run feature engineering and agglomerative clustering
Industry:Retail
Problem: Optimize website/app flow to increase checkout completion.
Method: Bayesian A/B Testing, Sequential Analysis, Power Analysis.
Key Metrics: Posterior Probability, Expected Loss Reduction.
Solution: Implement winning variant or pause underperformers early.
Approach: Bayes Factor vs. p-value for early stopping. Use Alpha Spending functions for sequential tests. Compute posterior probability of lift
Industry:Retail
Problem: Optimize experimentation while maximizing immediate revenue (vs. standard A/B testing).
Method: Thompson Sampling, Bayesian Optimization, Contextual Bandits.
Key Metrics: Cumulative Regret, Revenue per Session.
Solution: Real-time personalization engine updates.
Approach: Balance Explore vs. Exploit Trade-off, using Gaussian Process Surrogates. Apply dynamic variant allocation based on user context
Industry:Retail
Problem: Did our ad cause the purchase, or would they have bought anyway?
Method: Causal Inference (Double Machine Learning), T-Learners/X-Learners.
Key Metrics: Uplift Lift, True Incremental Sales.
Solution: Target "persuadable" users, ignore "sure things".
Approach: Use Heterogeneous Treatment Effects (HTE) and Propensity Score Matching / Weighting. Use libraries for uplift modeling (DoWhy, EconML, causalift, causalml)).
Industry:Retail
Problem: Predict future revenue from individual customers.
Method: Bayesian Hierarchical Modeling, Survival Analysis, GLM.
Key Metrics: NCLV (Net CLV), Predicted Repurchase Probability.
Solution: Segment high CLV users for retention campaigns.
Approach: Pareto/NBD or Beta-Geo Negative Binomial Distribution; Bayesian Updating of CLV parameters. Fit a model to transaction history (P1, P2, T).
Industry:Retail
Problem: Balance stock availability vs. holding costs.
Method: ARIMA/SARIMA, Prophet (Additive Time Series), State-Space Models.
Key Metrics: MAPE/WMAPE, Coverage of Prediction Interval.
Solution: Automated replenishment triggers.
Approach: Perform Trend/Cycle/Seasonality Decomposition of parallel time series. Determine Bayesian Forecast Intervals (Prediction Bands), through Univariate time series fitting..
Industry:Retail
Problem: Attribute sales to different marketing channels (TV, Digital, Search).
Method: Bayesian Hierarchical Regression, Time Series Decomposition.
Key Metrics: Media ROI, Saturation Points, Carry-over Effect.
Solution: Budget reallocation across channels for Q+1 planning.
Approach: Handle Heteroscedasticity & Multicollinearity in input data. Calculate Lagged Effects on Sales. Apply Bayesian regression using PyMC or Stan
Industry:Retail
Problem: Determine optimal price points to maximize revenue/profit.
Method: GLM with Interaction Terms, Causal Inference (IV), Bayesian Optimization.
Key Metrics: Elasticity Coefficients, Revenue per Unit.
Solution: Dynamic pricing rules or promotional discount thresholds.
Approach: Apply Demand Curve Fitting (Non-linear regression). Determine Instrumental Variables for Price Endogeneity
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AI/ML Data Scientist
Successful projects, happy customers.
List of blog articles on selected topics relevant to the projects in progress or completed.
GenAI
Automated product tagging and product compatibility annotation from outfit pictures.
Forecasting using Deep Learning
How to apply deep learning for forecasting for parallel time series: LSTML, DeepAR.
If you would like to know more about our services, insights from completed use cases or design/implementation details, please leave us a message, and we will contact you ASAP.