Staff Data Scientist

A day in the life (Responsibilities)

Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives.
Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights.
Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy.
Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization.
Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise.
Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward.
Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.

What you'll need to thrive (Requirements)

7+ years of experience in data science with a proven track record of delivering production ML systems that drive measurable impact.
Deep knowledge of statistical modeling, machine learning (e.g., tree-based models, time series, deep learning), and model evaluation.
Experience working with real-world product data at scale and translating ambiguous problems into well-scoped ML solutions.
Experience with distributed data processing and training, real-time inference, and ML Ops frameworks
Prior experience mentoring other data scientists or acting as a tech lead.
Experience leading experimentation (e.g., A/B testing), causal inference, and real-time decision systems.
Proficiency in Python and SQL, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD.
Hands-on experience with cloud platforms (preferably AWS), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock.
Excellent communication skills and the ability to influence both technical and non-technical stakeholders.
Strong business acumen with the ability to align technical solutions with company goals.
Experience building services on top of LLMs in a large scale production environment.
Bonus ingredients*:

An advanced degree in Computer Science, Statistics, or a related STEM field is preferred.
Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability.
Experience fine-tuning LLMs and applying reinforcement learning from human feedback (RLHF) to improve model performance and alignment.

Skills Required
5+ years of experience in data science with production ML systems
Deep knowledge of statistical modeling and machine learning (tree-based models, time series, deep learning)
Experience working with real-world product data at scale and translating ambiguous problems into ML solutions
Experience with distributed data processing and training, real-time inference, and MLOps frameworks
Prior experience mentoring other data scientists or acting as a technical lead
Experience leading experimentation (A/B testing), causal inference, and real-time decision systems
Proficiency in Python and SQL
Experience with ML frameworks (scikit-learn, PyTorch, TensorFlow)
Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD
Hands-on experience with cloud platforms (preferably AWS) including SageMaker, Athena, Glue, DynamoDB, Bedrock
Excellent communication skills and ability to influence technical and non-technical stakeholders
Strong business acumen and ability to align technical solutions with company goals
Advanced degree in CS, Statistics, or related STEM field
Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability
Experience fine-tuning LLMs and applying RLHF