Sr. Data Scientist

Resolution Technologies, Inc.; Atlanta (GA); On-site; USD 150,000 - 190,000; Full time

Job summary

Resolution Technologies, Inc. seeks a Senior Data Scientist to own end‑to‑end ML solutions within a pharmaceutical client engagement. You will build models, transform data, and manage ingestion pipelines, taking raw data to production-grade ML while shaping the operating model and MLOps framework.

You will work independently within the client’s team, applying reusable frameworks from our data and AI practice, and contribute to production ML capabilities as the engagement scales.

Qualifications

• Proven experience building and deploying ML models in production.
• Strong Python and ML libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch).
• Strong data engineering skills: SQL, data transformation, and pipeline development (dbt, Snowflake-native, or equivalent).
• Hands-on Snowflake experience for warehousing, transformation, and ingestion.
• Hands-on AWS SageMaker experience across the model lifecycle.
• MLOps tooling: CI/CD for ML, monitoring, automated retraining, and version control.
• Ability to work independently and reliably in a staff augmentation capacity as part of the client’s team.

Responsibilities

• Build, train, and validate ML models for predictive analytics use cases.
• Develop and maintain data transformation pipelines in Snowflake.
• Automate ingestion and processing of new data feeds with latency considerations.
• Own the full ML lifecycle in AWS SageMaker (or Snowflake-native ML).
• Implement MLOps practices: CI/CD, model versioning, pipelines, monitoring.
• Evaluate Snowflake environment and recommend cost-effective path before production.
• Contribute to a blueprint for the ML operating model and collaboration with stakeholders.

Skills

Python programming

ML libraries

SQL

Snowflake

AWS SageMaker

MLOps

Independent worker

Tools

dbt

Airflow

Step Functions

Terraform

CloudFormation

Docker

Snowflake

Job description

Senior Data Scientist

About the Role: Our client is looking for a Senior Data Scientist for a long‑term engagement with a pharmaceutical client. This is a hands‑on, end‑to‑end role embedded directly within the client’s data and AI team. The data scientist builds machine learning models and predictive analytics solutions and owns the data engineering behind them—including transformation, cleanup, and automated ingestion of new data—as well as the full MLOps lifecycle. The client is establishing its production ML capability and does not yet have a model running in production, so this person should be comfortable taking raw, messy data all the way to a monitored production model and helping shape the operating model and MLOps framework as they go. This individual operates independently as a trusted member of the team while bringing the collective experience, methodologies, and reusable frameworks of our broader data and AI practice to bear.

Primary Responsibilities

• Build, train, and validate ML models supporting predictive analytics use cases (e.g., batch/resource optimization and exception pattern recognition in a manufacturing setting).
• Develop and maintain data transformation and cleanup pipelines in Snowflake, turning raw source data into modeling‑ready datasets.
• Automate ingestion and processing of new and recurring data feeds, accounting for data that may arrive from external systems or partners with latency.
• Own the full ML lifecycle in AWS SageMaker (or Snowflake‑native ML where appropriate): training, deployment, monitoring, retraining, and drift detection.
• Implement MLOps practices: CI/CD for ML, model versioning, automated pipelines, and performance monitoring.
• Evaluate the client’s existing environment—including Snowflake’s native MLOps capabilities—and recommend the most cost‑effective, fit‑for‑purpose path before building from scratch.
• Contribute to a blueprint for the client’s ML operating model: where models are trained, how they are promoted and deployed, and how users access outputs (e.g., dashboards or batch inputs).
• Collaborate with stakeholders to translate business requirements into production solutions, and deliver work phase by phase, starting with the lowest‑effort, highest‑value use cases.

Required Qualifications

• Proven experience building and deploying ML models in production.
• Strong Python and ML libraries (scikit‑learn, XGBoost, TensorFlow, or PyTorch).
• Strong data engineering skills: SQL, data transformation, and pipeline development (e.g., dbt, Snowflake‑native, or equivalent).
• Hands‑on Snowflake experience for warehousing, transformation, and ingestion.
• Hands‑on AWS SageMaker experience across the model lifecycle.
• MLOps tooling: CI/CD for ML, monitoring, automated retraining, and version control.
• Ability to work independently and reliably in a staff augmentation capacity as part of the client’s team.

Preferred Qualifications

• Prior experience in pharma, life sciences, or another regulated industry.
• Familiarity with GxP, HIPAA, or 21 CFR Part 11.
• Understanding of data privacy and validation requirements in regulated settings.

Additional Skills

• Orchestration (Airflow, Step Functions)
• Infrastructure‑as‑code (Terraform/CloudFormation)
• Containerization (Docker)

What Sets a Strong Candidate Apart

Above all, we value honesty about capability. The client is looking for a resource whose demonstrated experience matches their resume—someone they can trust to assess the landscape, make sound technical judgments, and pick up new areas (such as agentic or generative AI) as the engagement matures. A candidate who can build production ML solutions while helping mature the surrounding framework will be an ideal fit.