Data Scientist Consultant

Role & responsibilities

Key Responsibilities:

• Predictive Modeling: Create and deploy sophisticated predictive models to improve underwriting precision,

risk evaluation, and pricing strategies. Utilize machine learning algorithms to enhance claims forecasting,

detect fraudulent activities, lead generation etc. Assess and modify existing models based on the business

requirements and industry standards.

• Data Analysis: Examine extensive datasets on policyholders, claims and market trends to uncover patterns

and insights. Offer actionable recommendations to boost business performance and operational efficiency.

• Algorithm Development: Develop, construct, and refine algorithms for risk modeling, customer

segmentation, and loss forecasting. Ensure that the models are both robust and scalable.

• Fraud Detection: Create and enhance models to identify fraudulent activities and minimize fraudulent

claims. Apply techniques for anomaly detection and pattern recognition.

• Customer Insights: Build and support models to analyze customer behavior, preferences, and lifetime value.

Formulate strategies to improve customer retention and drive acquisition.

• Project Leadership: Spearhead data science projects from initial conceptualization to final implementation.

Work closely with insurance analysts, actuaries, IT teams, and business stakeholders to ensure successful

project execution and delivery.

• Data Visualization: Create interactive dashboards and reports to clearly convey findings to both technical

and non-technical stakeholders. Present insights and recommendations to senior management effectively.

• Research & Development: Keep up to date with industry trends, emerging technologies, and best practices

in data science. Investigate and implement cutting-edge techniques to ensure a competitive advantage.

• Compliance: Ensure that all data science practices are in compliance with regulatory requirements and data

privacy standards specific to the insurance industry.

• Performance tracking: Collaborate with business stakeholders to define, evaluate, and track key business

outcomes and KPIs related to the data science project deliverables.

Preferred candidate profile

• Tools & Technologies: Experience with data visualization tools (e.g., Qliksense, Tableau, Power BI) and data

processing platforms (e.g., Apache Airflow).

• Proficiency in programming languages such as Python, Pyspark, R, or Scala.
• Experience with machine learning frameworks/platforms (e.g., Vertex AI, TensorFlow, PyTorch, scikit-learn)

and statistical analysis.

• Familiarity with big data technologies (e.g., Hadoop, Spark) and databases (e.g., SQL, NoSQL).
• Soft Skills: Exceptional analytical and problem-solving skills. Excellent communication abilities with the

capacity to translate complex technical information into business insights.

• Experience: Individual should have 3+ years of experience in data science, with a focus on the insurance or

financial services industry.

• Technical Skills: Must have experience with Google cloud computing platforms and their data services.