Data Scientist III

GForce Life Sciences; Princeton (NJ); On-site; USD 120,000 - 170,000; Full time

Job summary

GForce Life Sciences in Princeton, NJ seeks an experienced Data Scientist to design and maintain scalable data pipelines, build and deploy machine learning models, and uncover opportunities to automate analytics. The role blends data engineering, applied data science, and innovation to transform raw data into reliable insights for business decisions.

The candidate should have strong SQL and Python skills, experience with Snowflake, and a track record delivering production models while

Qualifications

• Bachelor's degree in a quantitative or technical discipline; Master's preferred.
• 5–7 years of experience in data science, analytics, or data engineering.
• Strong SQL and Python proficiency with modular, tested code.
• Experience building and maintaining production data pipelines in cloud data warehouses (Snowflake preferred).
• Hands-on experience with statistical modeling, ML, feature engineering, and model validation.

Responsibilities

• Design, build, and maintain production data pipelines transforming raw data into analysis-ready datasets.
• Develop reusable feature tables and shared data assets for modeling efficiency.
• Write modular, tested SQL and Python for data transformations.
• Implement automated data quality checks for freshness, completeness, and anomalies.
• Optimize pipelines in Snowflake for performance, cost, and scale.
• Perform exploratory data analysis to guide statistical analyses and modeling.
• Engineer features and develop, validate, and refine ML models (forecasting, attribution, segmentation, propensity).
• Transition models from prototype to production with supporting data pipelines.
• Monitor deployed models for drift and improve as needed.
• Translate results into actionable business recommendations for stakeholders.
• Evaluate emerging technologies (Generative AI, LLMs, modern data stack) and automation opportunities.
• Develop reusable tools, templates, and standards across the team.
• Collaborate with governance and IT to establish data contracts, lineage, and documentation.

Skills

SQL

Python

Analytical thinking

Communication

Problem-solving

Education

Bachelor's in quantitative/technical field

Master's preferred

Tools

Snowflake

Job description

Our client is seeking an experienced Data Scientist to develop and maintain scalable data pipelines, build and deploy machine learning models, and identify opportunities to improve analytics through automation and emerging technologies. This role combines data engineering, applied data science, and innovation, with a focus on transforming raw data into reliable, actionable insights that support business decisions. The ideal candidate has strong SQL and Python skills, experience working with cloud data warehouses such as Snowflake, and the ability to take analytical solutions from initial development through production deployment.

Responsibilities

• Design, build, and maintain production data pipelines that transform raw data into analysis-ready and model-ready datasets.
• Develop reusable feature tables and shared data assets to improve modeling efficiency and consistency across projects.
• Write modular, well-tested SQL and Python code to support data transformations and pipeline development.
• Implement automated data quality checks to monitor data freshness, completeness, schema changes, and anomalies.
• Optimize data pipelines within cloud data warehouses, such as Snowflake, for performance, cost, and scalability.
• Perform exploratory data analysis to identify patterns, evaluate data readiness, and determine opportunities for statistical analysis and modeling.
• Engineer features and develop, validate, and refine statistical and machine learning models, including forecasting, attribution, segmentation, and propensity models.
• Transition models from prototype to production, including developing supporting data pipelines and integrating model outputs.
• Monitor deployed model performance, identify model or data drift, and implement improvements as needed.
• Translate analytical and modeling results into clear, actionable recommendations for business stakeholders.
• Evaluate and prototype emerging technologies, tools, and frameworks, including Generative AI, large language models (LLMs), agentic automation, and modern data stack solutions.
• Identify opportunities to automate repetitive analytics and modeling processes.
• Develop reusable tools, templates, and standards that can be adopted across the broader team.
• Partner with business stakeholders, data governance, and IT teams to establish data contracts, lineage, and documentation.

Qualifications

• Bachelor's degree in a quantitative or technical discipline, such as Physics, Computer Science, Applied Mathematics, Statistics, or a related field; Master's degree preferred.
• 5–7 years of relevant experience in data science, analytics, data engineering, or a related field.
• Strong proficiency in SQL and Python, including experience developing modular, tested data transformations.
• Experience building and maintaining production data pipelines and working with cloud data warehouses, preferably Snowflake.
• Hands‑on experience with statistical modeling, machine learning, feature engineering, and model validation.
• Experience deploying models into production and monitoring ongoing model performance.
• Understanding of automated data quality monitoring, data governance, and data documentation practices.
• Experience evaluating and implementing automation tools, emerging technologies, and AI/ML solutions.
• Strong analytical, problem‑solving, and communication skills, with the ability to translate complex findings into actionable business recommendations.