JOB DUTIES

Senior Data Scientist with strong Life Insurance domain expertise to design, develop, and deploy advanced analytical and machine learning models that support pricing, underwriting, persistence, claims, and customer lifecycle optimization.

JOB REQUIREMENTS

Senior Data Scientist

English Level: Professional proficiency (written and spoken)

Key Responsibilities

Data Science & Advanced Analytics

• Synthesize large, complex datasets to uncover trends, assess business impact, and recommend data-driven improvements.

• Design and build statistical and machine learning models to support Life Insurance use cases such as:

○ Pricing and product profitability

○ Underwriting risk assessment

○ Lapse & persistency prediction

○ Customer lifetime value (CLV)

○ Cross-sell and upsell optimization

• Build GLM-based and other statistical models aligned with Life Insurance actuarial and pricing methodologies.

• Develop explanatory, predictive, and forecasting models using descriptive and inferential statistics, regression, and machine learning techniques.

• Research, evaluate, and implement appropriate statistical and mathematical methodologies for business problems.

Data Preparation & Exploration

• Perform data validation, preprocessing, feature engineering, and exploratory data analysis (EDA).

Ensure model inputs are accurate, scalable, and compliant with business and regulatory requirements.

Leadership & Mentorship

• Provide guidance and mentorship to junior data scientists and analysts.

• Promote best practices in model development, documentation, and knowledge sharing.

Experience & Domain Knowledge

• 5+ years of industry experience in data science, analytics, or applied machine learning.

• Strong hands-on experience in the Life Insurance domain (pricing, underwriting, claims, or customer analytics).

• Solid understanding of actuarial concepts and insurance risk modeling principles.

Technical & Analytical Skills

• Extensive experience with supervised and unsupervised machine learning algorithms, including Generalized Linear Models (GLMs).

• Strong understanding of predictive and prescriptive analytics.

• Advanced experience in:

○ Data manipulation and feature engineering

○ Exploratory data analysis

○ Model development and solution design

• Strong programming experience in Python.

• Strong programming experience in SQL.

• Working knowledge of Git version control.

• Ability to communicate effectively with both technical and non-technical audiences in written, oral, and presentation formats.

• Strong problem-solving skills with the ability to multi-task and learn quickly

Preferred Qualifications

• Prior experience in Life Insurance actuarial, pricing, underwriting, or risk analytics.

• Advanced knowledge of model tuning, evaluation, validation, and operationalization (MLOps).

• Experience designing and consuming APIs at scale.

• Hands-on experience with cloud and big data technologies, such as:

○ Databricks / Spark

○ Azure, AWS, or GCP

○ Snowflake

• Experience with Deep Learning frameworks (TensorFlow / Keras, PyTorch, MXNet).

• Experience in Text Analytics and Natural Language Processing (NLP) (e.g., claims notes, underwriting documents).

• Comfortable with command-line environments (Linux / Windows scripting).

Experience with additional programming languages (e.g., R, Scala, Julia, Go, Java, or C++).

Education

• Graduate degree preferred in Statistics, Computer Science, Data Science, Mathematics, Economics, Engineering, or a related technical field.

Equivalent practical experience will be considered