• Lead ML and Product Data Science organizations delivering production AI across LLMs, RAG, ranking/scoring, personalization, growth, monetization, and revenue analytics.

• Define product data strategy for large, ambiguous problem spaces; translate company OKRs into multi-quarter analytics, experimentation, and ML roadmaps aligned to business impact.

• Drive product analytics: north-star metric definition, KPI trees, funnel optimization, retention modeling, cohort analysis, LTV modeling, and growth acceleration across the user lifecycle.

• Own experimentation at scale: A/B testing platforms, hypothesis design, power analysis, guardrail metrics, incrementality measurement, causal inference, and launch decision frameworks.

• Build LLM-powered products end-to-end: RAG pipelines, vector search, embeddings, prompt engineering, tool use, guardrails, evaluation (LLM-as-judge), and cost/latency/quality optimization.

• Ship model improvements via fine-tuning, distillation, prompt tuning, domain adaptation, feature engineering, and model performance optimization.

• Establish scalable data foundations: instrumentation strategy, event taxonomy, logging standards, data contracts, metric governance, and SQL-first data warehousing (batch + real-time).

• Architect production-grade ML systems: CI/CD, containers, cloud deployment, monitoring/observability, model versioning, drift detection, rollback strategies, and reliability engineering.

• Lead cross-functional strategy with Product, Engineering, Sales, Marketing, and GTM; translate business goals into measurable experimentation and ML execution plans.

• Influence executive decision-making through opportunity sizing, forecasting, scenario modeling, capacity planning, and business case development.

• Operate hands-on across Python, SQL, distributed systems, APIs, experimentation frameworks, and production services; bridge research, engineering, and product delivery.

• Led 3 data science teams (Load and Haul, Drill and Blast, Safety), working cross functionally with the business, product, and executives to deliver impactful solutions to the mining industry.

• Delivered more than $450 million in value to the mining business by optimizing roads, trucks, and operations.

• Led the development and deployment of a proprietary Generative AI-based application for the mining industry, significantly enhancing pit supervisors' ability to identify, prioritize, and address operational issues

• Strategic advisor to senior leaders on commercialization strategy, external partnerships, customer success, and long-term roadmaps.

• Hired, mentored and led a team of data scientists and data engineers to successfully develop over 10 digital products that increase operational efficiency and reduce costs

• Led data driven innovation at Teck by setting objectives, outlining roadmaps, and guiding the team to deliver several research initiatives.

• Founder of the "Safety Analytics" team with a focus on safeguarding worker well-being and ensuring the sustainability of the operations

• Designed end-to-end machine learning systems, collaborating closely with stakeholders to develop models that bolster business operations.

Instructed high-performing students from software development backgrounds in the following specialized programs:

• Generative AI for Business with Microsoft Azure Open AI

• Generative AI for NLP (Transformers and Large Language Models, Self-Attention mechanism, Embeddings, Retrieval Augmented Generation, Natural Language Querying, Langchain)

• Data Science on Cloud (Azure and AWS)

• Massachusetts Institute of Technology Applied Data Science Program

Medium's top writer in Artificial Intelligence since 2022

Publishing novel ideas in Data Science with more than 1k views per day

• Served major global OEM clients across North America, Europe, and Asia, leading the development of technology assets for automotive applications across data analytics, automation, and intelligent systems.

• Built Python-based analytics and machine learning prototypes to analyze vehicle, sensor, and operational data, supporting use cases such as performance monitoring, anomaly detection, predictive modeling, and decision support.

• Developed SQL-driven data workflows to extract, clean, transform, and analyze structured datasets from engineering, testing, and operational systems.

• Created internal tools and lightweight web applications to help engineering and business stakeholders explore data, review model outputs, and operationalize analytical insights.

• Partnered with cross-functional teams across software, controls, systems engineering, and client-facing delivery to translate automotive business problems into scalable technical solutions.

• Designed reusable technology assets, including data processing scripts, analytical models, dashboards, and proof-of-concept systems that accelerated delivery across multiple client engagements.

• Applied statistical analysis, machine learning, and engineering domain knowledge to identify patterns, diagnose system behavior, and recommend improvements for automotive applications.

• Supported end-to-end delivery from requirements gathering and technical design through prototyping, validation, stakeholder demos, and client handoff.