Data Scientist Ii

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.

It is expressly understood that the aforementioned obligations and responsibilities are not exhaustively stated, so that the Employee must comply with all other functions, obligations and responsibilities, limitations or instructions of the Company that derive from everything related to his main activities and without additional compensation since the Employee's salary already includes any compensation required. This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required.

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