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Grant Gajkowski
Verified Expert in Engineering
Data Science Developer
Tucson, AZ, United States
Toptal member since March 23, 2026
Bio
Grant is a senior data scientist with deep experience spanning enterprise analytics, machine learning, and AI-enabled product development. He has built statistical frameworks, predictive models, KPI systems, and decision-support tools at Apple and LendingClub. He also develops production AI applications, including NLP pipelines and agentic workflows that turn unstructured data into structured, scalable products.
Portfolio
Apple
Agentic AI Systems, Causal Inference, Data Science, Python, Machine Learning...
LendingClub
Causal Inference, Data Science, Python, SQL, Tableau, Microsoft Power BI...
County of Santa Clara, CA
Data Science, Python, Tableau, Microsoft Power BI, Data Modeling, SQL...
Experience
- Statistical Analysis - 8 years
- Python - 8 years
- SQL - 8 years
- Data Science - 8 years
- Experimental Design - 8 years
- Machine Learning - 6 years
- Predictive Modeling - 6 years
- Agentic AI Systems - 3 years
Preferred Environment
Python, Snowflake, Jupyter
The most amazing...
...thing I’ve developed is a production AI CRM for Gmail that turns live email threads into structured deal records, then uses ML to prioritize and surface them.
Work Experience
Research Manager
2024 - 2025
Apple
- Served as lead researcher for companywide talent analytics, designing and delivering statistical analyses, causal inference studies, and predictive models to inform decisions on performance, skills, and related employee and business outcomes.
- Designed, built, and implemented a scalable Python framework for statistical testing and data mining, accelerating the discovery of high-signal differences across employee segments.
- Applied causal inference, regression, and tree-based modeling to investigate drivers of companywide workforce outcomes and support high-priority business decisions.
- Delivered rapid-turn analyses on companywide workforce and business data, turning ambiguous questions into decision-ready recommendations.
- Developed and maintained KPI pipelines, metric definitions, and QA checks to improve reporting consistency and stakeholder trust in data.
- Translated analytical findings into executive-ready presentations and readouts, supporting high-stakes decision-making with clear narratives.
- Standardized recurring analytics workflows, improving reliability, reducing manual effort, and speeding delivery of stakeholder insights.
- Designed Tableau dashboards for non-technical users, improving clarity, usability, and actionability of complex reporting.
Technologies: Agentic AI Systems, Causal Inference, Data Science, Python, Machine Learning, SQL, Snowflake, Tableau, Experimental Design, LightGBM, Predictive Modeling, Scikit-learn, Pandas, Statistical Analysis, Data Visualization, Artificial Intelligence (AI), Large Language Models (LLMs), APIs, AI Agents, RAG Systems, Agentic AI, AI Automation, Jupyter, AI Architecture, PostgreSQL, Retrieval-augmented Generation (RAG), Vector Databases, Time Series, Time Series Analysis, Claude Code
Senior Analytics Manager
2021 - 2024
LendingClub
- Delivered end-to-end analytics and data science solutions using Python, SQL, and BI tools, from problem framing and data preparation through analysis, dashboards, and stakeholder rollout.
- Applied causal inference, hypothesis testing, A/B testing, regression, and survival analysis to measure impact, quantify drivers, and support business decision-making.
- Built predictive models using regression and tree-based models (LightGBM, XGBoost) to identify key drivers, evaluate trends, and inform business and operational strategy.
- Developed governed datasets, metric definitions, and standardized reporting foundations to improve reliability, consistency, and self-service analytics.
- Transformed manual reporting processes into automated dashboards and recurring analytics workflows, improving efficiency, scalability, and decision speed.
- Partnered closely with stakeholders to structure ambiguous business questions into clear analytical frameworks and data-backed recommendations.
- Designed dashboards and reporting tools that made complex data more accessible and actionable for cross-functional business teams.
Technologies: Causal Inference, Data Science, Python, SQL, Tableau, Microsoft Power BI, Machine Learning, Predictive Modeling, Scikit-learn, LightGBM, Pandas, Statistical Analysis, Data Visualization, Artificial Intelligence (AI), Large Language Models (LLMs), APIs, AI Agents, RAG Systems, Agentic AI, AI Automation, Jupyter, AI Architecture, PostgreSQL, Retrieval-augmented Generation (RAG), Time Series, Time Series Analysis
Senior Analytics Manager
2018 - 2021
County of Santa Clara, CA
- Earned three promotions in two years, advancing into senior analytics leadership through strong technical delivery, stakeholder partnership, and business impact.
- Built an analytics program and data products supporting services for 20,000+ employees, improving visibility into key operational and workforce metrics.
- Owned analytics delivery end-to-end, from requirements gathering and technical design through dashboard development, rollout, and stakeholder enablement.
- Designed and deployed self-service dashboards and automated reporting tools using SQL, Power BI, Python, and R for managers and executives.
- Developed forecasting models and quantitative analyses to support workforce planning, labor strategy, and other high-stakes business decisions.
- Delivered automated dashboards and recurring reports to 700+ managers and executives, expanding access to consistent KPI tracking and decision support.
- Created quantitative models supporting workforce planning and labor negotiations affecting approximately 14,000 employees and contributing to $5+ million in cost savings.
Technologies: Data Science, Python, Tableau, Microsoft Power BI, Data Modeling, SQL, Predictive Modeling, Machine Learning, Statistical Analysis, Data Visualization, Pandas, APIs, Jupyter, PostgreSQL, Time Series, Time Series Analysis
Experience
Deckata
http://www.deckata.comAn AI-enabled analytics application for HR and people teams that turns raw workforce data into executive-ready presentations and reporting outputs. I designed and built the product end-to-end, including the data model, transformation pipelines, analytics layer, application logic, and AI workflow orchestration.
The system ingests messy HR source data, normalizes it into a governed analytical model, pre-aggregates key metrics, and supports low-latency reporting and presentation generation. I also implemented AI-driven workflows with structured outputs, tool use, and validation patterns to improve reliability and reduce failure modes. The result was a full-stack decision-support product that combined analytics engineering, machine learning, and application development to automate a high-friction reporting process.
The system ingests messy HR source data, normalizes it into a governed analytical model, pre-aggregates key metrics, and supports low-latency reporting and presentation generation. I also implemented AI-driven workflows with structured outputs, tool use, and validation patterns to improve reliability and reduce failure modes. The result was a full-stack decision-support product that combined analytics engineering, machine learning, and application development to automate a high-friction reporting process.
Orchid CRM
http://www.orchidstack.comAn AI-enabled CRM and workflow automation product built directly inside Gmail for talent and marketing agencies. I designed and built the system end-to-end, including the Chrome extension front end, back-end APIs, data model, inbox sync architecture, and AI extraction workflows.
The product ingests live email activity, processes unstructured threads, and converts them into structured deal and contact records that stay up to date within the user’s existing workflow. I implemented production patterns for reliability, including structured outputs, validation logic, tool use, and workflow guardrails to improve extraction quality and downstream usability. I also built the supporting sync and event-processing infrastructure to handle mailbox updates and keep records up to date. The result was a full-stack AI product that reduced manual CRM entry and gave users real-time visibility into deals, next steps, and pipeline activity without requiring them to leave Gmail.
The product ingests live email activity, processes unstructured threads, and converts them into structured deal and contact records that stay up to date within the user’s existing workflow. I implemented production patterns for reliability, including structured outputs, validation logic, tool use, and workflow guardrails to improve extraction quality and downstream usability. I also built the supporting sync and event-processing infrastructure to handle mailbox updates and keep records up to date. The result was a full-stack AI product that reduced manual CRM entry and gave users real-time visibility into deals, next steps, and pipeline activity without requiring them to leave Gmail.
Education
2013 - 2017
Bachelor's Degree in Psychology (Neuroscience Track)
University of Arizona - Tucson, Arizona, USA
2013 - 2017
Bachelor's Degree in Economics
University of Arizona - Tucson, Arizona, USA
Skills
Libraries/APIs
Pandas, Scikit-learn
Tools
Tableau, Microsoft Power BI, Jupyter, Claude Code
Languages
Python, SQL, Snowflake
Storage
PostgreSQL
Frameworks
LightGBM
Other
Machine Learning, Data Science, Causal Inference, Experimental Design, Predictive Modeling, Data Visualization, Statistical Analysis, Artificial Intelligence (AI), APIs, Time Series, Time Series Analysis, Agentic AI Systems, Data Modeling, Natural Language Processing (NLP), Large Language Models (LLMs), AI Agents, RAG Systems, Agentic AI, AI Automation, AI Architecture, Retrieval-augmented Generation (RAG), Vector Databases
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