Who we are at Embroker
Embroker is the digital insurance company committed to transforming business insurance. With a radically simple approach, Embroker is replacing the 1970’s technology of the insurance industry, with a first-ever end-to-end digital insurance platform that provides better coverage and reduced cost; all in minutes, not hours. Founded in 2015 and headquartered in San Francisco, Embroker has raised over $150M of funding from leading fintech investors.
Radically Simple > Needlessly Complex.
The value of this position
Embroker is looking for an experienced data engineer to be a founding member of our Data Science team. The Data Science team will be a top-level department within Embroker, reporting into the CTO. This organizational design is meant to elevate the team’s accountability and influence, by making the team responsible for directly impacting the business. More specifically, the team is responsible for revenue and other metrics and will define its own roadmap to achieve their goals. Most critically, the team will have the agency to innovate and develop net new capabilities. The goal is that this innovation will provide step-function change in the operation of the business and create strategic advantages that differentiate Embroker from the competition. You are passionate about leading initiatives to deliver capabilities that are rigorous and reliable. Your work will be leveraged throughout the company including product, sales, marketing, services, and leadership. You have deep technical skills and are excited about building a green field data platform to drive the business forward.
What you will own in this role
- The chief responsibility is to put Data Science into production with enterprise capabilities (performance, scale, security).
- Define and develop the program and architecture for data collection, modeling, metrics creation, data validation, model training, and reporting of intelligence.
- Drive the collection of new data and the refinement of existing data sources.
- Create pipelines (data processing, data analysis, optimization, implementation, validation)
- Leverage appropriate analytic strategy, design, or methods of data collection necessary to generate reliable, comprehensive, and deep insights.
What experience we think is the right fit
- Hands-on experience with several languages (R, Python, Java, Scala JS, SQL, etc.) not only to manipulate data and draw insights from diverse data sets, but also to integrate models into production services.
- Delivering internal/production data tools for ETL, experimentation, exploration, cleansing, reconciliation.
- Working knowledge of OLTP, OLAP, and other novel datastores (MySQL, Snowflake, Redshift, RDS, NoSQL [binary/json], Hive, HDFS, SolR, Elastic Search)
- Working knowledge of distributed data/computing tools: Map / Reduce, Hadoop, Spark, etc. and the data formats leveraged principally by these technologies (JSON, Parquet, Avro, Raw Text)
- Defining and developing the program and architecture for data collection, modeling, metrics creation, data validation, model training, and reporting of intelligence.
- Driving the collection of new data and the refinement of existing data sources.
- Define data schemas and services, focused on accessibility/use-case for the consuming process (NoSQL, Graph DB, Parquet/HDFS, SolR/ElasticSearch
- Creating reporting and present visualizations that tell a story and provide insight
- Assisting with developing models to project key business metrics (Traffic, Conversion, Engagement) and developing KPIs to drive optimization and improvement of product features and business strategies.
- Working cross-functionally to define problem statements, collect data, build analytical models, and drive solutions
Experience that is nice to have
- Bachelors in Mathematics, Physics, Statistics, Computer Science, Engineering, or another quantitative field. Masters preferred.
- Visualizing/presenting data for stakeholders using Looker
- Analyzing data from 3rd party providers: Google Analytics, Adwords, Segment, Salesforce, Pardot, Mandrill, Facebook Ads, etc.
- Leading projects involving cross-functional and cross-cultural teams, fostering relationships across areas of the business that include Marketing, Sales, Product, Services, and Engineering.
- Familiarity with machine learning and statistical approaches (Clustering, Decision Trees, Bayesian, GLM/Regression, Random Forest, Boosting, Neural Networks, etc.)
- Domain knowledge in insurance, financial services, advertising, or marketing industry
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.