Data Scientist

Evlo AI; Raleigh (NC); On-site; USD 110,000 - 160,000; Full time

Job summary

Evlo AI seeks an experienced data scientist to own applied analytics from exploration to production deployment. You will design causal studies, build predictive models, and craft data pipelines at scale, collaborating with ML engineers, PMs, and data engineers to drive decisions in a fast-paced product environment.

You will translate complex insights into actionable narratives for leadership, contribute to reproducibility, and help establish production-grade standards across research and

Qualifications

• 3–6 years of experience in data science, applied statistics, or machine learning.
• Advanced SQL fluency and strong Python skills (pandas, NumPy, scikit-learn, statsmodels).
• Hands-on experience with gradient boosting frameworks (XGBoost, LightGBM) and time-series or forecasting methods.
• Practical knowledge of causal inference and experimentation: diff-in-diff, propensity matching, uplift modeling, or A/B test design.
• Experience with big data tooling: Spark (PySpark), Databricks, Snowflake, or equivalent at scale.
• MS or PhD in statistics, computer science, economics, applied math, or a related quantitative field.
• Bonus: experience with causal ML libraries (DoWhy, EconML), stream processing (Kafka, Flink), or publishing/applied research experience.

Responsibilities

• Design and execute statistical analyses, causal inference studies, and controlled experiments (A/B tests) to answer product and business questions with statistical rigor.
• Build, validate, and productionize predictive models - regression, gradient boosting (XGBoost/LightGBM), time-series forecasting, and classic ML - on datasets at terabyte scale.
• Develop data pipelines and feature engineering workflows in Python and SQL, leveraging Spark or Snowflake for large-scale transformation.
• Partner with MLOps and ML engineering teams to deploy models via REST APIs or batch pipelines, including monitoring for drift and performance decay.
• Design experiment frameworks: power analysis, randomization strategy, metric definitions, and post-test statistical inference.
• Translate complex findings into clear, decision-ready narratives for engineering leadership and executive stakeholders.
• Contribute to team standards for reproducibility, code review, and documentation across research and production codebases.

Skills

Advanced SQL

Python (pandas, NumPy, scikit-learn, 0

Gradient boosting (XGBoost, LightGBM)

Time-series forecasting

Big data tooling (Spark, PySpark)

Education

MS or PhD in statistics, computer science, economics, applied math, or related quantitative field

Tools

XGBoost

LightGBM

Spark

Databricks

Snowflake

Job description

About The Role

The role owns applied data science work on high-stakes problems: forecasting, causal inference, experimentation, and statistical modeling on large-scale behavioral and operational data. Models and analyses built in this role ship to production and drive decisions measured in millions of dollars.

About The Role

The role owns applied data science work on high-stakes problems: forecasting, causal inference, experimentation, and statistical modeling on large-scale behavioral and operational data. Models and analyses built in this role ship to production and drive decisions measured in millions of dollars.

This is not a dashboard-only role. The data scientist partners directly with ML engineers, product managers, and data engineers to take work from exploratory analysis to deployed, monitored systems - and is expected to own outcomes, not just notebooks.

Key Responsibilities

• Design and execute statistical analyses, causal inference studies, and controlled experiments (A/B tests) to answer product and business questions with statistical rigor
• Build, validate, and productionize predictive models - regression, gradient boosting (XGBoost/LightGBM), time-series forecasting, and classic ML - on datasets at terabyte scale
• Develop data pipelines and feature engineering workflows in Python and SQL, leveraging Spark or Snowflake for large-scale transformation
• Partner with MLOps and ML engineering teams to deploy models via REST APIs or batch pipelines, including monitoring for drift and performance decay
• Design experiment frameworks: power analysis, randomization strategy, metric definitions, and post-test statistical inference
• Translate complex findings into clear, decision-ready narratives for engineering leadership and executive stakeholders
• Contribute to team standards for reproducibility, code review, and documentation across research and production codebases

What We Are Looking For

• 3-6 years of experience in data science, applied statistics, or machine learning, with at least one model or analysis pipeline taken to production
• Advanced SQL fluency and strong Python skills (pandas, NumPy, scikit-learn, statsmodels)
• Hands-on experience with gradient boosting frameworks (XGBoost, LightGBM) and time-series or forecasting methods
• Practical knowledge of causal inference and experimentation: diff-in-diff, propensity matching, uplift modeling, or A/B test design
• Experience with big data tooling: Spark (PySpark), Databricks, Snowflake, or equivalent at scale
• MS or PhD in statistics, computer science, economics, applied math, or a related quantitative field
• Bonus: experience with causal ML libraries (DoWhy, EconML), stream processing (Kafka, Flink), or publishing/applied research experience