Overview

We are seeking a skilled and experienced Data Scientist II to join our growing analytics team. This role is ideal for someone with a strong foundation in data science who has demonstrated success independently executing complex analytics projects and developing production-grade machine learning solutions. As a Data Scientist II, you will take a lead role in designing analytical approaches, delivering impactful insights, mentoring junior team members, and contributing strategically to cross-functional initiatives. You will work closely with internal stakeholders and external customers to solve real-world problems, with a strong emphasis on healthcare and insurance analytics.

Responsibilities

• Problem Framing & Solution Design: Collaborate with stakeholders to understand business challenges and define data science solutions that align with goals.
• Data Exploration & Analysis: Perform exploratory data analysis to uncover trends, detect anomalies, and guide decision-making. Extract actionable insights that inform product, operational, or strategic decisions.
• Statistical Analysis & Experimentation: Design and validate hypotheses using appropriate statistical techniques (e.g., A/B testing, regression, causal inference). Support data-driven decisions through experimentation and quantitative analysis.
• Model Development & Evaluation: Build and fine-tune machine learning models using structured and unstructured data for tasks such as prediction, classification, segmentation, and ranking. Select appropriate algorithms and rigorously evaluate model performance using statistical and domain-specific metrics.
• Model Deployment & Maintenance: Work with engineering teams to deploy models into production, ensuring they are scalable, reliable, and maintainable. Monitor and retrain models as needed to maintain performance over time and changing data.
• Insight Communication & Visualization: Deliver insights through clear storytelling, visualizations, and reports tailored to technical and non-technical audiences. Build dashboards to enable self-service access to data insights.
• Cross-Functional Collaboration: Partner with product, engineering, business, and other teams to integrate data science solutions into workflows and products. Contribute to metric definitions and measurement strategies.
• Mentorship & Knowledge Sharing: Mentor junior team members and contribute to a culture of learning and technical excellence. Share best practices, reusable code, and thought leadership to improve team efficiency and impact.
• Continuous Learning & Innovation: Stay current with industry trends and emerging tools in data science and AI. Evaluate new methods and technologies for impact and efficiency gains.
• Complete all responsibilities as outlined in the annual performance review and/or goal setting.
• Complete all special projects and other duties as assigned.

Qualifications

Education & Experience:

• Typically requires a Bachelor's degree in relevant fields such as data science, computer science, economics, statistics, mathematics or other quantitative fields and a minimum of 5 years of relevant experience;
• OR Master's degree with a minimum of 3 years of relevant experience;
• OR PhD with no experience.

Technical Proficiency:

• Minimum of 5 years of hands-on experience in data science, with a demonstrated ability to own and deliver end-to-end projects.
• Strong proficiency with machine learning libraries such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Expert-level proficiency in Python and SQL for data manipulation and analysis.
• Familiarity with big data frameworks (Spark, Hadoop) and data pipeline tools (Airflow, DBT) is a plus.
• Experience with Auto-ML tools such as DataRobot or OCI is a plus.
• Proficient with version control systems like Git and agile development tools such as JIRA.
• Strong analytical and statistical skills, with the ability to interpret and visualize insights effectively.
• Ability to translate business requirements into technical / analytic solutions and product features.

Communication & Collaboration:

• Excellent written and verbal communication skills with the ability to engage both technical and non-technical stakeholders.
• Fully bilingual in English and Spanish (written and verbal).
• Ability to work independently and in a self-organized team environment using agile methods.
• Highly proficient in Microsoft Office (PowerPoint, Excel, Word).