About the job

SummaryBy Outscal

Fairmatic seeks a Senior Data Scientist with 5+ years of experience in machine learning, statistics, and data analysis to develop risk models for insurance. Proficiency in Python, ML frameworks, and SQL is essential. Experience with telematics data and risk models is advantageous.
At Fairmatic we're on a mission to make roads safer, one fleet at a time. 

Fairmatic is revolutionizing the auto insurance industry by using data and AI to personalize options and incentivize safe driving with savings. Our predictive risk models have been trained with 200 billion miles of driving data and tested with hundreds of thousands of paying drivers. 

Fairmatic's leadership team includes serial entrepreneurs, insurance industry innovators, and startup veterans who have raised over $88M in funding in less than a year. 

Join our global team of curious, adaptable technologists and problem solvers who are passionate about creating a positive impact on the world!

We are looking for a talented Senior Data Scientist for our Risk Intelligence team. Risk Intelligence is at the core of everything we do. In this role, you will research, develop and improve the machine learning models predicting claims frequency, claims severity and fraud detection. You will work on automating claims payouts and enhancing other aspects of rating, claims, and customer experience (e.g. insight to improve fleet safety). This role involves continuous experimentation, A/B testing, and an iterative approach to productizing our learnings. You will work closely with product management, underwriting, actuaries, machine learning engineers and other product/engineering teams.

A day in the life

    • Use advanced statistics, machine learning, and mathematical approaches to develop highly predictive risk models to improve our underwriting and claims settlement processes
    • Assess Fairmatic’s modeling techniques, identify improvements and communicate results with internal stakeholders, including leadership, to ensure these are incorporated into business processes
    • Lead R&D efforts for doing POCs to validate new product ideas and experiments to improve existing predictive models
    • Design data collection strategies and their applications for building new products
    • Provide technical mentorship to other team members
    • Exemplify and foster Fairmatic’s humble, collaborative and impact-obsessed culture

What you will need:

    • MSc/Ph.D. in Statistics, Machine Learning, or a related field, with 5+ years of experience as a data scientist.
    • High proficiency in statistics, machine learning algorithms, and data manipulation and analysis.
    • Extensive experience in research-oriented roles, analyzing business problems from a data science perspective, conducting literature research, and implementing and testing solutions.
    • Expertise in supervised and unsupervised machine learning models (e.g., GLM, GAM, XGBoost, Deep Learning).
    • A proactive approach to staying updated with the latest developments in AI and incorporating them to enhance existing products.
    • Hands-on programming experience with Python.
    • Experience with machine learning frameworks such as Keras or PyTorch, and libraries like scikit-learn and pandas.
    • Proficiency in SQL.Proficiency in visualizing complex data and extracting actionable insights.
    • A product-driven mindset with a knack for transforming research outcomes into impactful products.
    • Excellent communication skills, with the ability to articulate experiment goals and methodologies clearly and communicate insights effectively to the team.
    • Experience with risk models and insurance companies - Advantage
    • Experience with telematics data and solution - Advantage
    • Experience in deploying production solutions - Advantage
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About The Company

Tel Aviv District, Israel (Hybrid)

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Karnataka, India (Hybrid)

Tel Aviv District, Israel (Hybrid)

Tel Aviv District, Israel (Hybrid)

Tel Aviv District, Israel (Hybrid)

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Tel Aviv District, Israel (Hybrid)

Tel Aviv District, Israel (Hybrid)

Lower Silesian Voivodeship, Poland (Hybrid)

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