
Senior Data Scientist
- Bangalore, Karnataka
- Permanent
- Full-time
- Assist in gathering, cleaning, and preprocessing data from various sources, ensuring data quality and consistency.
- Conduct exploratory data analysis (EDA) to identify trends, patterns, and insights within the data.
- Building predictive models to enhance accuracy.
- Test and evaluate basic machine learning models, including linear and logistic regression, decision trees, and clustering algorithms.
- Create clear and informative data visualizations and reports to communicate insights to business stakeholders.
- Tracking key metrics and performance indicators related to business objectives.
- Collaborate with business and technical teams to understand project requirements and contribute to the design of data-driven solutions.
- Participate in team meetings, brainstorming sessions, and discussions on data science projects and initiatives.
- Stay updated on the latest trends and tools in data science, machine learning, and the insurance industry.
- Participate in ongoing training and development programs to enhance technical skills and industry knowledge.
- Education: Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, or a related field. A Master's degree is a plus.
- Experience : 4+ years of experience in data science or analytics
- Basic Industry Knowledge: General understanding of insurance concepts (e.g., policies, claims, risk, customer lifecycle) is desirable but not required.
- Insurance Data Familiarity: Some experience working with structured data (e.g., customer demographics, policy details) and an eagerness to learn about the specific types of data used in the insurance industry.
- Predictive Models: Exposure to basic predictive modeling techniques such as regression, classification, forecasting or clustering. Experience with insurance-specific models (e.g., claims prediction, risk assessment) is a plus.
- Data Preparation: Knowledge of data cleaning and preprocessing techniques, with experience in handling datasets to prepare them for analysis.
- Programming Skills: Proficiency in Python for data analysis, with familiarity in using libraries such as Pandas, NumPy, and Scikit-learn.
- Data Manipulation: Basic skills in SQL for querying and extracting data from databases.
- Statistical Knowledge: Understanding of fundamental statistical concepts, including distributions, probability, hypothesis testing, and descriptive statistics.
- Curiosity & Willingness to Learn: Enthusiastic about learning new concepts, techniques, and industry-specific knowledge in the insurance domain.
- Problem-Solving Skills: Ability to approach challenges analytically and think critically about data and project requirements.
- Attention to Detail: Keen eye for detail in data handling and model development to ensure high-quality outcomes.
- Communication Skills: Clear and concise communication skills, with the ability to present findings and insights effectively.