Data Scientist

Data Scientist - Job Overview

About CLARA

CLARA Analytics is the leading AI as a service (AIaaS) provider that improves casualty claims outcomes for commercial insurance carriers and self-insured organizations. The company’s product suite for workers comp, commercial auto and general liability insurance claims applies image recognition, natural language processing, and other AI-based techniques to unlock insights from medical notes, bills and other documents surrounding a claim. CLARA’s customers include companies from the top 25 global insurance carriers to large third-party administrators and self-insured organizations. Founded in 2017, CLARA Analytics is headquartered in California’s Silicon Valley. For more information, visit www.claraanalytics.com, and follow the company on LinkedIn and X.

About the Role

We are seeking a highly motivated and curious Data Scientist to join our dynamic team. This role is perfect for a problem-solver who is passionate about the entire machine learning lifecycle, from idea to production, and is driven to build impactful AI solutions. You will tackle complex challenges by transforming large-scale data into intelligent products. We are looking for a strong technical talent with a passion for continuous learning and a commitment to quality, eager to apply their skills to solve meaningful business problems. While experience in the insurance or financial sector is a plus, your drive and skill are what matter most.

What You’ll Do...

Innovative Research & Development

• Explore, prototype, and implement cutting-edge algorithms and methodologies to solve complex business challenges and drive product innovation.
• Proactively stay current with the latest advancements in AI and machine learning, driven by curiosity, and champion new technologies and techniques within the team.

Full-Cycle Model Development & Management

• Conduct thorough Exploratory Data Analysis (EDA) to uncover insights and inform modeling strategies.
• Design, build, train, and evaluate robust machine learning models, with opportunities to work on NLP problems using state-of-the-art architectures (e.g., transformers).
• Develop and execute strategies for retraining models to adapt to new data and evolving patterns.
• Take ownership of model performance in production, identifying drift, and ensuring sustained accuracy and reliability.

ML Engineering & Best Practices

• Write clean, well-tested, and maintainable code, applying best practices in ML engineering to build scalable, reproducible solutions.
• Create and maintain clear documentation for code, models, and processes to support team collaboration and long-term maintainability.
• Actively work to improve technical debt and elevate the team's engineering standards.

Cross-Functional Collaboration

• Work closely with multi-disciplinary teams, including product managers, analysts, and data engineers, to ensure AI solutions are well-defined, technically feasible, and aligned with business goals.
• Act as a bridge between technical and business teams, translating business needs into data science problems.

Communication & Knowledge Sharing

• Translate complex quantitative concepts into clear, actionable insights for both technical and non-technical stakeholders.
• Contribute to a culture of shared knowledge and data-driven decision-making.

Ethics and Responsible AI

• Ensure all AI solutions adhere to ethical guidelines and data privacy regulations (e.g., GDPR, HIPAA).
• Incorporate explainable AI (XAI) methods to build transparency and trust in model outcomes.

What We’re Looking For...

REQUIRED

Education:

• MS or higher in a quantitative discipline (e.g., Artificial Intelligence, Statistics, Computer Science, Mathematics, Physics, Electrical Engineering).

Experience:

• Minimum 3 years of experience in developing and deploying machine learning models in a production environment.

Technical Expertise:

• Proficient in Python and experienced with core data science libraries (e.g., Pandas, NumPy, Scikit-learn).
• Proficiency with Git for version control and collaborative development.
• Hands-on experience with modern machine learning frameworks including LightGBM, XGBoost, or deep learning frameworks like PyTorch and TensorFlow.
• Familiarity with ML engineering tools and platforms.
• Solid understanding of core machine learning concepts, algorithms, and evaluation metrics.

The Right Mindset:

• Natural curiosity and a drive to ask questions, dig deeper, and learn continuously.
• A strong sense of ownership and pride in the quality of your work, from initial analysis to production code.
• An organized and methodical approach with a commitment to creating good documentation.

Communication:

• Excellent ability to articulate and present complex technical concepts and results to diverse audiences.

PREFERRED

Advanced Degree:

• PhD in a quantitative discipline.

NLP Experience:

• Demonstrable experience or a strong interest in Natural Language Processing (NLP), including working with modern libraries and frameworks (e.g., Hugging Face Transformers).

ML Engineering:

• Hands-on experience with ML engineering tools and platforms in a production environment (e.g., MLFlow, Spark).

Domain Experience:

• Experience in the insurance or finance industry.

Specialized Skills:

• Prior experience implementing explainable AI (XAI) techniques to build transparency and trust in model outcomes.
• Familiarity with advanced ML approaches such as graph neural networks, Agentic AI, or causal inference.

Track Record:

• Prior experience in integrating AI solutions within the insurance industry, with a focus on claims or underwriting.
• A history of publishing research or participating in industry competitions is highly desirable.

What We Offer...

• The opportunity to make a real impact on a growing company.
• Work on challenging and rewarding projects that will push your technical skills.
• Collaborative and supportive work environment.
• Competitive salary and benefits package (employer-provided health insurance and ancillary benefits, flexible PTO, fully WFH, 401k with match, etc.)
• Be a part of a team that is passionate about what we do.