Lead Data Scientist

People In AI; United States; Remote; USD 200,000 - 220,000; Full time

Job summary

Join a forward-thinking organization as a Lead Data Scientist, where you will shape core retrieval and ranking infrastructure. This role emphasizes high-leverage work in AI and machine learning, focusing on developing impactful ranking and recommendation systems. Collaborate with a skilled team in a remote-first environment that values innovation and efficiency. If you're passionate about leveraging data to drive decision-making and thrive in a dynamic setting, this is an exciting opportunity to make a real difference.

Benefits

Vision Insurance

Medical Insurance

401(k) Plan

Qualifications

• 6+ years of experience in data science or machine learning.
• Expertise in ranking and recommendation systems.

Responsibilities

• Develop ranking and retrieval systems using optimization and modeling.
• Lead lifecycle of model development from design to deployment.

Skills

Data Science

Machine Learning

Optimization

Collaborative Filtering

Statistical Modeling

Python

Education

Bachelor's Degree in Computer Science or related field

Master's Degree in Data Science or related field

Tools

PyTorch

TensorFlow

XGBoost

Spark

Airflow

Job description

This range is provided by People In AI. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$200,000.00/yr - $220,000.00/yr

Direct message the job poster from People In AI

Placing the Top AI and Machine Learning Talent

Lead Data Scientist (Ranking & Recommendation Systems)

Remote (US Only)

$200,000 – $220,000 Base + Bonus

Are you ready to take ownership of upstream modeling efforts at a mission-driven organization focused on intelligent prioritization and large-scale decision systems? We're looking for an exceptional Lead Data Scientist to shape core retrieval and ranking infrastructure that drives real impact.

Why This Opportunity?

High-Leverage Retrieval & Ranking Work

• Design algorithms that surface the most relevant actions, claims, or data in complex operational workflows.
• Tackle core challenges in entity ranking, recommendation, and prioritization using structured and semi-structured data.
• Drive offline evaluation, experiment design, and principled iteration using business- and model-facing metrics.

A Focused, Highly Technical Team

• Join a small, fast-moving group of scientists and engineers with deep experience in optimization, ML systems, and large-scale modeling.
• Work in a culture that values clean modeling, strong architecture, and scientific rigor — not just Jupyter notebooks.
• Collaborate closely with product and engineering to turn modeling insights into production-grade solutions.

Remote-First, No Bureaucracy

• Fully remote (U.S.-based candidates only), with flexible hours and an emphasis on deep, focused work.
• Lean teams, minimal overhead, and strong execution culture — a place for people who like to build and move fast.

What the Role Involves

• Develop ranking and retrieval systems from first principles using optimization, probabilistic modeling, and hybrid recommenders.
• Lead the full lifecycle of model development — from objective definition and data exploration to deployment and post-launch evaluation.
• Analyze large-scale behavioral and transactional data to inform model design and uncover latent patterns.
• Own offline experimentation frameworks, design A/B tests, and contribute to a metrics-driven culture of iteration.
• Mentor junior team members and help set standards for experimentation, reproducibility, and performance.

What We're Looking For

• 6+ years of experience in data science, machine learning, or optimization, ideally with ownership of ranking/recommendation systems.
• Expertise in collaborative filtering, entity ranking, or personalized retrieval pipelines.
• Strong foundation in numerical optimization, statistical modeling, and ML evaluation techniques.
• Production-level coding experience in Python and proficiency with frameworks like PyTorch, TensorFlow, or XGBoost.
• Experience with data tooling and pipelines (e.g., Spark, Airflow), and a solid grasp of system performance tradeoffs.
• Ability to translate ambiguous, high-level problems into measurable, testable solutions — and then execute.

This is a rare opportunity to lead upstream modeling in a production environment, working on high-impact systems that blend precision science with real-world complexity. If you're energized by retrieval, ranking, and recommender systems — and you want your models to matter — we’d love to hear from you.

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Science, Engineering, and Information Technology

• Industries

  Hospitals and Health Care, Retail, and Research Services

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Inferred from the description for this job

Vision insurance

Medical insurance

401(k)

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