Data Scientist (Head of Data Science Track)

Findevor is an innovation company dedicated to democratizing opportunity through premium AI that enhances decision-making and elevates human intelligence. Our mission is to empower insurers with the tools to make faster, smarter, and more profitable decisions by solving unsustainable inefficiencies in portfolio management and risk underwriting.

We are building an agentic AI platform to address over $300B in market inefficiencies across the insurance sector. By automating complex workflows across data analysis, triage, and strategic simulation, Findevor enables underwriters and executives to unlock profitable growth with greater speed and confidence. Our latest focus is on maximizing profitable growth by improving premium and loss leakage through an AI Orchestrated workflows.

Our co-founders have successfully scaled an AI fintech startup to IPO and bring deep technical and commercial expertise.

As Data Scientist, you’ll design and implement data strategies that power the intelligence behind Findevor’s platform. You’ll play a critical role in building out our data infrastructure and analytics capabilities, and as we grow, you’ll have the opportunity to step into the Head of Data Science and potentially eventually our Chief Science Officer role.

In addition to platform analytics, this role will drive R&D efforts for scientific innovation—exploring novel approaches and model architectures with potential for patentable breakthroughs. You will help shape the company's patent strategy and contribute to its long-term technical differentiation in the market.

We are open to starting this role as a part-time contractor, with a planned transition to full-time employment and long-term leadership.

Reporting Structure: This role will report to the CTO on all matters directly related to product delivery and AI system integration. For research innovation, patent strategy, and long-term vision, the role will report directly to the CEO.

• Research & Innovation: Lead exploratory initiatives that push the boundaries of AI, data science, and modeling with a focus on breakthrough outcomes. Work on scientific advancements that could lead to proprietary technology or patents.

• Data Strategy & Infrastructure: Help design and evolve our data infrastructure to support scale, performance, and AI-driven decision-making. Collaborate with engineering to define and implement scalable data pipelines and repositories.

• Advanced Analytics: Conduct analysis on structured and unstructured data sources to generate insights that support underwriting, product development, and strategic decision-making.

• Scientific Thought Leadership: Stay ahead of industry trends, academic research, and emerging tools. Bring a research-oriented mindset to model evaluation, experimental design, and performance benchmarking.

• IP & Patent Development: Contribute to the company’s patent strategy by translating novel ideas into defensible intellectual property in collaboration with the CTO and CEO.

• Data Partnerships & External Sourcing: Support the identification and onboarding of external data providers and develop strategies to leverage data partnerships for competitive advantage.

• Team Building & Planning: Help assess what technical and human resources will be needed to grow the data science function and shape the roadmap for team expansion.

• Cross-functional Collaboration: Work closely with the CTO, CEO, and broader product team to align data initiatives with business strategy and technology direction.Work closely with the CTO and product teams to translate business goals into data-driven insights and features.

• Technical Stack: Strong skills in Python, SQL, and data science libraries (e.g., Pandas, Scikit-learn, PyTorch or TensorFlow).

• Data Engineering: Experience with data lake architectures, ETL pipelines, and cloud-native tooling (e.g., AWS Glue, Redshift, S3).

• Machine Learning: Hands-on experience building, training, and validating models for classification, regression, and clustering.

• Model Deployment: Familiarity with MLOps workflows and deploying models into production environments.

• Visualization & Reporting: Ability to communicate findings via tools like Streamlit, Plotly, Power BI, or dashboards.

• Domain Curiosity: Strong interest in financial risk modeling, insurance, or regulated industries a plus.

• Research Orientation: Passion for scientific discovery and interest in contributing to patentable innovation.

• Self-Directed Research: Demonstrated ability to lead independent investigations and translate research into actionable, real-world results.(e.g., Pandas, Scikit-learn, PyTorch or TensorFlow).

• Data Engineering: Experience with data lake architectures, ETL pipelines, and cloud-native tooling (e.g., AWS Glue, Redshift, S3).

• Machine Learning: Hands-on experience building, training, and validating models for classification, regression, and clustering.

• Model Deployment: Familiarity with MLOps workflows and deploying models into production environments.

• Visualization & Reporting: Ability to communicate findings via tools like Streamlit, Plotly, Power BI, or dashboards.

• Domain Curiosity: Strong interest in financial risk modeling, insurance, or regulated industries a plus.

• Research Orientation: Passion for scientific discovery and interest in contributing to patentable innovation.

• Education: PhD preferred. Candidates with a Master's degree in Data Science, Computer Science, Applied Mathematics, or a related field will also be considered.

• Experience: 2–5 years in data science or machine learning roles, ideally within a startup, financial services, or insurtech environment.

• Location: Remote, with preference for candidates based in Europe.

• Trust and Integrity: A commitment to honesty, transparency, and building a culture of trust and teamwork.

• Continuous Improvement and Growth Mindset: Always striving for improvement and embracing the journey to perfect individual and collective skills.

• Meritocracy and Excellence: Valuing the best ideas through healthy and constructive debate, regardless of title or pedigree.

• Customer-First and Problem-Focused: Prioritizing customer needs and dedicating efforts to delight users with superior solutions.

• Disciplined Innovation: Taking calculated risks for technological breakthroughs and fostering an environment where risk-taking is valued over punishing mistakes.

• Grit and Focused Execution: Demonstrating resilience and an “owner’s mentality” by focusing on achieving ambitious goals through disciplined thinking and acting.

• Kindness, Respect, and Integrative Work-Life: Valuing kindness, respect, and “tough love” in all interactions, and striving for meaningful work-life integration while maintaining a strategic vision and excitement for the future.

Alex Valdes, Co-founder & CEO

Feel free to message Alex on LinkedIn or reach out directly with any questions about the role, the company, or the application process.