An emerging analytics division within a leading financial services organization is seeking a talented Data Scientist to contribute to developing predictive models, delivering data-driven insights, and supporting strategic decision-making processes.
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

Data Scientist

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