Senior Data Scientist
Location: New York / Remote
When you join our client as a Senior Data Scientist, you’re joining a company that values development, career growth, collaboration, innovation, and diversity & inclusion. They want employees to feel proud about being part of a company that is committed to doing the right thing. Through various resources and programs, you can grow your career while developing personally and professionally.
This is a 50-person innovative corporate Analytics group within New York. They are a rapidly growing their entrepreneurial department which aims to design, create, and offer innovative data-driven solutions for many parts of the enterprise.
In the 5 years of the existence of this project they have created a lot of predictive modeling solutions that are being used by various areas in the company. They have also built a modern model deployment platform that allows models to be accessed in real time or batch (via APIs) from any production system in the company.
They work with data ranging from demographics, credit and geo data to detailed medical data (medical test results, diagnosis, prescriptions) and social media information. They have a modern computing environment with a solid suite of data science/modeling tools and packages, and a large (but manageable) group of well-trained professionals at various levels to support you. Life insurance is on the verge of huge change. This is a chance to be part of, actually to drive, the transformation of an industry.
Is this not why we became data scientists?
You will apply your highly developed analytical skills to work on all aspects of the life insurance value chain, ranging from risk models, fraud detection, customer behavior study, process triaging, and marketing prediction to a variety of other analytics solutions.
You will apply your technical data/analytical/programming skills to ingest, wrangle and explore external and internal data to create data assets and reports, function as the data expert and prepare data for modeling and support production deployment of models, and to build world-class machine learning models for solving tangible business problems.
You will apply your high energy level and business sense to communicate with internal stakeholders and external vendors while effectively contributing to complex analytics projects.
- Leads and contributes to data analysis and modeling projects from project/sample design, business review meetings with internal and external clients deriving requirements/deliverables, reception and processing of data, performing analyses and modeling to final reports/presentations, communication of results and implementation support.
- Demonstrates to internal and external stakeholders how analytics can be implemented to maximize business benefits. Provides technical support, which includes strategic consulting, needs assessments, project scoping and the preparation/presentation of analytical proposals.
- Utilizes advanced statistical and machine learning techniques to create high-performing predictive models and creative analyses to address business objectives and client needs. Tests new statistical and machine learning analysis methods, software and data sources for continual improvement of quantitative solutions.
- Implements analytical models into production by collaborating with technology and operation teams. Utilizes proper data visualization tools for model testing, modeling results and data patterns exhibition. Design performance metrics for model selection and performance monitoring.
- Utilizes data wrangling/data matching/ETL techniques while programming in several languages to explore a variety of data sources, gain data expertise, perform summary analyses and prepare modeling datasets. Deploys analytical solution in production systems.
- Proactively and effectively communicates in various verbal and written formats with internal stakeholders on product design, data specification, model implementations, with partners on collaboration ideas and specifics, with clients and account teams on project/test results, opportunities, questions. Resolves problems and removes obstacles to timely and high-quality project completion.
- Creates project milestone plans to ensure projects are completed on time and within budget. Provides high quality ongoing customer support; answering questions, resolving problems and building solutions.
- Follows industry trends in insurance and related data/analytics processes and businesses. Functions as the analytics expert in meetings with other internal areas and external vendors. Actively participates in proof of concept tests of new data, software and technologies. Shares knowledge within Analytics group.
- Assures compliance with regulatory and privacy requirements during design and implementation of modeling and analysis projects.
- Travels to events and vendor meetings as needed (< 10%).
- Master’s degree with concentration in a quantitative discipline such as statistics, computer science, mathematics or machine learning and 5 years of relevant industry experience OR Ph.D. with concentration in similar fields and 2 years of relevant industry experience
- 2+ years of hands-on experience in coding and predictive modeling using large and complex datasets in a business setting
- Demonstrated capability to develop and deploy data science solutions and create values with limited guidance
- Strong verbal and written communications skills, listening and teamwork skills, and effective presentation skills. This is absolutely essential since you will have a lot of exposure to different internal groups (data, IT, actuarial, medical, underwriting, Legal, Agency, government relations, etc.) as well as third-party data partners
- Substantial programming experience with Python, R, SQL, and SPARK. Experience with data wrangling, data matching, and ETL techniques while programming in several languages (Python, R, SQL and Spark) to extract and transform data from a variety of data sources (Oracle, SQL, Hadoop)
- Strong expertise in supervised and unsupervised machine learning models (e.g., XGBoost, deep learning, k-means). Strong expertise in regularization techniques (Ridge, Lasso, elastic nets), variable selection techniques, feature creation (transformation, binning, high level categorical reduction, etc.) and validation (hold-outs, CV, bootstrap). Experience with data visualization (e.g. R Shiny, Spotfire, Tableau)
- Experience building pipeline for cleaning, parsing, and labeling of the unstructured text data is a plus.
- Experience using open source tools and libraries for Natural Language Processing, entity recognition, topic identification, and Regular Expression is a plus.
- Deep statistical knowledge and experience in survival analysis is a plus.
- Experience with insurance or consumer financial data is a plus.
- Experience with AWS and GitHub/GitLab is a plus.