Who we are at Embroker

Embroker makes commercial insurance simple. 

Since 2015, our team has worked to bring the insurance industry into the 21st century and beyond. Backed with $150M in funding, Embroker is creating the go-to business insurance for high-growth companies. Our digital-first experience combines the best policies with the right rates that fit customer needs.

Nothing is possible without our team. As part of the Embroker Pack, you will have a direct and enormous impact on daily operations, team interactions, and culture. You’ll build cool things, meet great people, and grow with us. All from the comfort of your couch.

We are helping businesses plan for tomorrow, so they can change the world today. Are you in?

The value of this position 

The company has been incredibly successful in selling its flagship business insurance product to institutional backed startups. Now, the company is expanding this offering to all private companies.The data science team has been tasked with optimally pricing this product and creating an automated premium quoting engine for Cyber Insurance using machine learning. This position is 100% remote based in India. 

What you will own in this role 

As a founding member of the data science team, the data scientist will be an integral part of optimizing premium pricing, predicting tail risk triggers, and creating an automated price quoting engine. The data scientist:

• Will have two to five years of data science and/or machine learning experience
• Is excited about expanding use cases for existing models, making improvements to current models, and working closely with business to put models into production.

After some experience, the data scientist would be also involved in:

• Identifying new datasets,
• Researching optimal algorithms to reduce portfolio loss, and
• Deriving insights to improve products and customer experience.

As a fully remote organization, the candidate will need to be self-driven.

What experience we think is the right fit 

Core competencies:

•   Hands-on experience with data-centric language (Python) not only to manipulate data and draw insights from diverse data sets but also to integrate models into production services.
•   Hands-on experience in various Python libraries (Matplotlib, Numpy, Pandas, and Scikit-Learn) and Jupyter Notebook
•   Knowledge and experience in statistical and data mining techniques, e.g., regression, clustering, classification
•   Working cross-functionally to define problem statements, collect data, build analytical models, and drive solutions
•   Ability to communicate data driven stories to technical- and non-technical audience

Preferred competencies:

•   Hands on experience in Keras and SQL
•   Familiarity with neural networks, natural language processing, recommender systems
•   Domain knowledge in insurance, financial services, advertising, or marketing industry

To apply:

•   Resume
•   Link to Github

Our Pack at Embroker lives our values

• Pack First
  We succeed and fail as one team. We always optimize for what is best for our entire organization. We communicate honestly and openly, treat each other with mutual respect, and assume positive intent in interactions. 

• Create Magic
  We deliver delightful experiences at every customer touchpoint and dedicate ourselves to make each one exceptional. We build transformational world-class products by applying our full creativity to find solutions to even the hardest problems. 

• Be All-In
  We make focused commitments. We are accountable to ourselves and each other to deliver on time. We 

• move fast and attack challenges with relentless positivity. We build things that make us proud.

We believe that systemic structures and practices disproportionately disadvantage the most marginalized people in society — including people of color, people from working-class backgrounds, women, and LGBTQ people. We believe that these communities must be represented and included in the work we do, to make our Pack stronger, more creative, and improve the way we do business. We strongly encourage applications from people with these identities or who are members of other marginalized communities.