Data Scientist

Together we fight for everyone’s opportunity for a better financial future.

We will do this together — with customers, partners and colleagues. We will fight for others, not against: We will stand up for and champion everyone’s access to opportunities. The status quo is not good enough … we believe every individual and every community deserves access to financial opportunities. We are determined to support both individuals and communities in reaching a better financial future.  We know that reaching this future depends on our actions today.

Like our Purpose Statement, Voya believes in being bold and committed to action.  We are committed to a work environment where the differences that we are born with — and those we acquire throughout our lives — are understood, valued and intentionally pursued. We believe that our employees own our culture and have a responsibility to foster an environment where we all feel comfortable bringing our whole selves to work. Purposefully bringing our differences together to positively influence our culture, serve our clients and enrich our communities is essential to our vision.

Are you ready to join a company with a strong purpose and a winning culture? Start your Voyage – Apply Now

Note: The desired location for this role is in CT in a commutable distance from the Windsor CT office. It will be a hybrid role (2-3 days a week in office).

Note: at this time we are not considering candidates who will require sponsorship now or in the future.

About the Role

We’re looking for a versatile Data Scientist to join our growing analytics team. In this role, you'll partner closely with business stakeholders to unlock value from data—whether that’s predicting advisor behavior, identifying growth opportunities, or automating manual workflows. You’ll be hands-on across the entire data science lifecycle: wrangling data, building and deploying machine learning models, and supporting the MLOps and infrastructure needed to scale solutions across the organization.

This is a high-impact role for someone who blends deep technical skill with business curiosity—and who is passionate about deploying ML models and analytics into the real world.

Key Responsibilities

• Data wrangling & feature engineering: Ingest, clean, and transform data from SQL, APIs, and data lakes (e.g., Snowflake, Databricks). Design robust pipelines that feed into analytics and ML workflows.
• Data understanding & exploration: Work closely with domain experts to deeply understand the meaning, context, quality, and limitations of available datasets. Translate business questions into data requirements and analytics plans.
• Machine learning development: Build, tune, and validate predictive models using scikit-learn, SparkML, XGBoost, or TensorFlow.
• Cross-functional partnership: Collaborate with marketing, sales, and product teams to scope business use cases, define success metrics, and integrate models into operational workflows.
• Model deployment & MLOps: Deploy and manage models using MLflow, docker and CI/CD pipelines. Implement versioning, testing, performance monitoring, and retraining strategies as part of a robust MLOps practice.
• Infrastructure support: Work with data engineering and DevOps teams to maintain and improve model training and deployment infrastructure, including compute resources, workflow orchestration and environment configuration.
• Insight delivery: Build clear, actionable reporting and visualizations using tools like Power BI or Tableau. Focus on impact, not just analysis.

Minimum Qualifications

• Bachelor’s degree in Data Science, Computer Science, Engineering, or a related quantitative field.
• 5+ years of experience in a data science, ML engineering, or analytics role.
• Strong SQL, Python and ML Techniques programming skills.
• Experience with Azure Cloud, Databricks, and/or Snowflake.

• Experience building and deploying machine learning models in production environments. Hands-on experience with Databricks, including SparkML, and MLflow integration.

• Familiarity with MLOps best practices, including version control, model monitoring, and automated testing.
• Experience with tools such as Git, MLflow, Docker and workflow schedulers.
• Ability to communicate complex technical work to non-technical stakeholders.
• Experience with scalable model training environments and distributed computing.

Preferred Qualifications

• Master’s degree in a quantitative or technical discipline.
• Experience in finan