Data Scientist Level 1

Job Title: Data Scientist Level 1

Location: Hybrid Newark, NJ

Duration: 12 months RTH

As a Senior Data Scientist on the GRI Data Science team, you will partner with our diverse team of Engineers, Economists, Computer Scientists, Mathematicians, Physicists, Statisticians, and Actuaries tasked with mining our industry-leading internal data to develop new analytics capabilities for our businesses. The role requires a rare combination of sophisticated analytical expertise, business acumen, strategic mindset, client relationship skills, problem-solving, and a passion for generating business impact. This is an exciting opportunity to be a part of a strategic initiative that is evolving and growing over time In addition to applied experience, you will bring excellent problem-solving, communication, and teamwork skills, along with agile ways of working, strong business insight, an inclusive leadership demeanor, and a continuous learning focus to all that you do.

Here is what you can expect in a typical day:

• Responsible for the hands-on development of sophisticated data science solutions comprising the portfolio developed by the Director of Data Science and the technical requirements specified by the Director of Data Science.

• Perform hands-on data analysis, model development, model training, model testing, and model deployment.

• Continuously research new methods for problem solution, including new algorithms, modeling techniques, and data analytics techniques.

• Write production-level code and partner with machine learning engineers to push development code into production.

• Partner with machine learning engineers to productionize machine learning models. Partner with data engineers to build data pipelines. Partner with software engineers to integrate solutions with business platforms.

• Work closely with the business and data science lead to recommend and develop models for customer engagement and wellness use cases.

• Manage external vendors in the execution of the data science development process.

The Skills and expertise you bring:

• Advanced degree (Master's, Ph.D.) in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines.

• Working on complex problems in which analysis of situations or data requires an in-depth evaluation of various factors. Exercises judgment within broadly defined practices and policies in selecting methods, techniques, and evaluation criteria for obtaining results.

• Knowledge of business concepts, tools, and processes that are needed for making sound decisions in the context of the company's business. Create and test hypotheses for customer engagement and wellness programs.

• Experience in research, designing experiments (ex, A/B testing), working with claims and customer experience data. A behavior science background is preferred but not required.

• Ability to learn creative skills and knowledge on an ongoing basis through self-initiative and solving challenges.

• Excellent problem-solving, communication, and collaboration skills.

Applied experience with several of the following:

• Data Acquisition and Transformation: Acquiring data from disparate data sources using API's, SQL, and NoSQL. Transform data using SQL, NoSQL, and Python. Visualizing data using a diverse tool set, including but not limited to Python and R.

• Database Management System: Knowledge of how databases are structured and function in order to use them efficiently may include multiple data environments, cloud/AWS, primary and foreign key relationships, table design, database schemas, etc.

o Knowledge of how to work with data from (do not build)

o SQL skills (relational) - CORE / Initial Proficiency

o Unstructured (NoSQL)

o Graph/ontology (DB Graph)

• Model Deployment: Understanding of: MDLC (Model Development Life Cycle), CI/CD/CT pipelines (using tools like Jenkins, CloudBees, Harness etc.), A/B testing. Pipeline frameworks like MLFlow, AWS SageMaker pipeline, etc. model and data versioning.

• Statistics and Computing: Exceptional understanding of: Calculus, Multivariable Calculus, Linear Algebra, Differential Equations, Probability, Statistics, Applied Probability, Applied Statistics, Computer Science (Programming Methodologies), and Cloud. Knowledge of statistical techniques such as the use of descriptive, inferential, Bayesian statistics, time series analysis, etc., to extract business insights and experimentation to solve business problems

• Data Wrangling: Preparing data for further analysis; Redefining and mapping raw data to generate insights; Processing of large datasets (structured, unstructured).

• Machine Learning: Understanding of machine learning theory, including the mathematics underlying machine learning algorithms. Expertise in the application of machine learning theory to building, training, testing, and monitoring machine learning models. Understanding and expertise in NLP (natural language processing).

• Programming Languages: Python, R, SQL, Java or Scala, SQL, Cypher