Data Scientist

Role Overview

This role involves designing and implementing advanced analytics solutions that influence investment strategies, portfolio management, and operational efficiencies. Collaborating with senior data scientists, product teams, and stakeholders, the Data Scientist will translate complex data into actionable insights that generate measurable business value.

Key Responsibilities

• Develop dashboards, reports, and visualizations to communicate insights and track key metrics for business stakeholders
• Build, evaluate, and refine predictive and forecasting models using statistical and machine learning techniques for various business use cases
• Collaborate with product managers, analysts, and business teams to gather requirements and develop tailored analytical solutions
• Clearly communicate findings and strategic recommendations through presentations and written reports
• Support data preparation, feature engineering, model validation, and ongoing performance monitoring of deployed models
• Contribute to enhancing analytics workflows, documentation standards, and best practices within the analytics team

Core Qualifications \& Requirements

• 2–4 years of hands-on experience in data science, analytics, or quantitative analysis roles
• Strong proficiency in SQL and programming languages such as Python, R, or similar for data analysis and modeling
• Proven experience developing predictive models and applying statistical or machine learning algorithms to solve business challenges
• Familiarity with data visualization tools such as Tableau, Power BI, or equivalent platforms
• Excellent analytical, problem-solving, and communication skills; ability to explain technical concepts to non-technical audiences
• Exposure to cloud computing environments, MLOps, or model deployment processes is preferred, but not mandatory

Nice-to-Have Qualifications

• Experience working within financial services, investment, or portfolio management environments
• Knowledge of cloud platforms such as AWS, Azure, or GCP
• Experience with model deployment, automation, or operational analytics workflows
• Familiarity with version control tools like Git and containerization technologies such as Docker
• Advanced degree (Master’s or Ph.D.) in Data Science, Statistics, Computer Science, or related fields

Core Technical Skills

• SQL (Structured Query Language), Python, R, BigQuery, Snowflake
• Machine learning algorithms (regression, classification, clustering, time-series forecasting)
• Data visualization (Tableau, Power BI, matplotlib, seaborn)
• Cloud platforms (AWS, Azure, GCP), MLOps, model deployment, containerization