Jeffrey Allard - Lead Data Scientist

Jeffrey Allard

Lead Data Scientist

San Francisco, California, United States

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Summary

👤

Senior

🎓

Top School

Jeffrey Allard is a Lead Data Scientist in San Francisco with 15 years of applied AI and econometrics experience and a track record of translating causal inference and experimental design into high-impact marketing decisions. He builds at-scale propensity, uplift, LTV, and next-best-action systems across enterprise stacks (Snowflake, Databricks, Spark) and has led ML deployments from vendor replacement to production DSP targeting. Jeffrey combines deep statistical rigor — synthetic controls, Bayesian MMM, contextual bandits — with hands-on engineering, contributing to Ray to improve dataset sampling, resource reporting, and compiled DAG memory optimizations. His background spans healthcare interventions, financial decision sciences, and global retail personalization, and he’s comfortable operationalizing LLMs for contextual labeling and insights. Known as an engineer who bridges research and production, he mentors teams and guides measurement strategies that consistently influence executive decisions.

8 years of coding experience

3 years of employment as a software developer

Seminars and Training

MS Applied Data Science, MS Applied Data Science at Central Connecticut State University

Independent Course Work

BA Economics (Honors), BA Economics (Honors) at Aquinas College - Grand Rapids

Github Skills (15)

ray10

tensorflow10

python10

data-engineering9

distribute9

machine-learning9

api8

apidoc8

performance-monitor7

testing7

performance-measurement7

performance-monitoring7

performance-tuning7

performance-analysis7

performance-analytics7

Programming languages (1)

Python

Github contributions (5)

ray-project/ray

Jul 2024 - Dec 2024

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Role in this project:

ML Engineer

Contributions:63 reviews, 5 PRs, 128 comments in 5 months

Contributions summary:Jeffrey focused on extending the Ray Dataset API to incorporate sample weights when using `ray.dataset.to_tf()`, enabling compatibility with `tensorflow.keras.model.fit`. They implemented changes to handle additional metadata within the TF datasets. Furthermore, the user addressed resource management, displaying pending actors separately in the progress bar and adding resource usage estimations for pending actors. Additional work includes separating outputs in compiled DAGs and optimizations to memory management within the compiled DAGs.

pythonconsistsruntimetensorflowserving

jeffreyjeffreywang/ray

Jul 2024 - Feb 2025

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Contributions:82 pushes, 9 branches in 7 months

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