Data Scientist

Position Details What You Will Do: Defining Problem Stements - Work closely with cross-functional teams, including analysts, product managers and domain experts to understand business requirements, formulate problem statements, and deliver relevant data science solutions. Machine Learning Model Development - Develop and optimize machine learning models by processing, analyzing and extracting data from varying internal and external data sources. Data Preprocessing – Own and manage complex ETL pipelines to clean, preprocess and transform large datasets. Feature Engineering - Identify and engineer relevant features to enhance model performance and accuracy Model Deployment and Evaluation – Design and implement robust evaluation metrics and frameworks to assess and monitor the performance of machine learning models Operational Management Develop algorithms and predictive models to solve critical business problems. Develop tools and libraries that will help analytics team members more efficiently interface with large amounts of data. Analyze large, noisy datasets and identify meaningful patterns that provide actionable results. Understand and recommend the best technology and/or tools to execute a data science/machine learning task in development and production. Create informative visualizations that intuitively display large amounts of data and/or complex relationships. Provide and apply quality assurance best practices for data science services across the organization. Develop, implement, and maintain version control and testing processes for modifications to algorithms and data analytics. Who You Are: A Master’s degree in Mathematics, Statistics, Data Analytics, Computer Science or directly related field. Preference will be given to candidates with PhD in the field of Science/Mathematics/Statistics and relevant experience in machine learning algorithms, tools and technology. Extensive experience solving analytical problems using quantitative approaches, preferably in the insurance sector. Comfortable in manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources using Python/R libraries and SQL Familiarity with relational, SQL and NoSQL databases. Knowledge of statistical analysis tools such as R, is a plus. Expert knowledge of scripting in Python using OOPS concepts Experience with PowerBI. Experience in using cloud AutoML tools including on Azure, Amazon, Google, etc Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and big data technologies. Experience in DevOps or MLOps is a plus Experience with DS or ML frameworks and libraries (e.g., Spark, TensorFlow, PyTorch) is a plus. Experience in owning and managing complex ETL pipelines using Control M, AirFlow is a plus. A strong passion for empirical research and for answering hard questions with data. A strong hunger to learn and use new and latest technologies and tools. Experience working in a team-oriented, collaborative environment. A flexible analytic approach that allows for results at varying levels of precision. Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner. Good written and verbal communication skills. Strong technical documentation skills. Good interpersonal skills. Keen attention to detail. Ability to effectively prioritize and execute tasks without supervision in a high-pressure environment.