Senior Data Scientist - Financial Modeling - Remote

Placements24; Stellenbosch; Hybrid; ZAR 900,000 - 1,500,000; Full time

Benefits offered by this job

Competitive salary

Fully remote with home office support

Cutting-edge projects

Health insurance

Retirement plan

Learning & development

Compute resources access

Flexible hours

Global team culture

Generous leave

Job summary

Placements24 is seeking a Senior Data Scientist with a focus on Financial Modeling to join a fully remote team serving the Banking & Finance sector. You will design, implement, and refine advanced models for risk, fraud, and forecasting, collaborating with traders and analysts across geographies.

The role requires 6+ years in data science with financial application experience, strong Python/R skills, and production deployment capabilities.

Qualifications

• Master's or PhD in a quantitative field (CS/Stats/Math/Physics/Economics).
• 6+ years of Data Science experience with financial modeling.
• Proficient in Python or R and ML libraries.
• Strong SQL and big data tech.
• Production model deployment experience.
• Strong communication and collaboration in cross-functional teams.
• Experience in Banking & Finance preferred.
• Remote work ability.

Responsibilities

• Design, develop, and implement advanced statistical and machine learning models for financial forecasting, risk management, and fraud detection.
• Analyze large, complex datasets to extract actionable insights and identify key drivers of financial performance.
• Build and maintain robust data pipelines and analytical infrastructure.
• Collaborate with quantitative analysts, traders, and product managers to translate business needs into data science solutions.
• Validate model performance, back-test, ensure interpretability and robustness.
• Stay updated on research and advancements in data science, ML, and financial modeling.
• Communicate complex findings and model methodologies clearly to technical and non-technical audiences.
• Mentor junior data scientists and contribute to team knowledge sharing and best practices.
• Develop strategies for data collection, cleaning, and feature engineering for financial applications.
• Optimize model performance and scalability for production environments.

Skills

Python

R

Machine Learning

Time Series

Statistical Modeling

Data Visualization

Education

Master's or PhD in a quantitative field

Tools

TensorFlow

PyTorch

scikit-learn

Pandas

NumPy

Spark

Hadoop

SQL

AWS

Azure

GCP

Job description

About the Role

Our client, a leader in innovative financial solutions, is actively seeking a highly skilled Senior Data Scientist with a specialization in Financial Modeling to join their fully remote team. This is a unique opportunity to leverage advanced data science techniques and machine learning algorithms to build and refine sophisticated financial models that drive critical business decisions. You will work on complex problems related to risk assessment, fraud detection, algorithmic trading, and predictive analytics within the Banking & Finance domain. This remote-first position requires a candidate with a strong analytical mindset, exceptional programming skills, and a deep understanding of statistical modeling and machine learning principles, applied specifically to financial contexts. You will be instrumental in enhancing our client's data-driven capabilities and contributing to their competitive advantage from anywhere in South Africa.

Key Responsibilities

• Design, develop, and implement advanced statistical and machine learning models for financial forecasting, risk management, and fraud detection.
• Analyze large, complex datasets to extract actionable insights and identify key drivers of financial performance.
• Build and maintain robust data pipelines and analytical infrastructure.
• Collaborate with quantitative analysts, traders, and product managers to understand business needs and translate them into data science solutions.
• Validate model performance, conduct rigorous back-testing, and ensure model interpretability and robustness.
• Stay abreast of the latest research and advancements in data science, machine learning, and financial modeling.
• Communicate complex findings and model methodologies clearly to both technical and non-technical audiences.
• Mentor junior data scientists and contribute to the team's knowledge sharing and best practices.
• Develop strategies for data collection, cleaning, and feature engineering relevant to financial applications.
• Optimize model performance and ensure scalability for production environments.

Requirements

• Master's or PhD in Computer Science, Statistics, Mathematics, Physics, Economics, or a related quantitative field.
• Minimum of 6 years of experience as a Data Scientist, with a strong focus on financial modeling and applications.
• Proficiency in programming languages such as Python or R, and relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn, Pandas, NumPy).
• Strong experience with SQL and big data technologies (e.g., Spark, Hadoop).
• Deep understanding of statistical modeling, machine learning algorithms, and time series analysis.
• Proven experience in building and deploying predictive models in a production environment.
• Excellent analytical, problem-solving, and critical thinking skills.
• Strong communication and presentation skills, with the ability to explain complex technical concepts.
• Experience in the Banking & Finance sector is highly preferred.
• Ability to work independently and manage projects effectively in a remote setting.
• Familiarity with cloud platforms (AWS, Azure, GCP) is a plus.

Benefits

• Highly competitive salary and performance-based bonuses.
• Fully remote work arrangement with full support for home office expenses.
• Opportunity to work on cutting-edge financial data science projects.
• Comprehensive health, dental, and vision insurance.
• Generous retirement savings plan with employer contribution.
• Continuous learning and development opportunities, including conference attendance.
• Access to powerful computing resources and data sets.
• Flexible working hours to ensure optimal work-life balance.
• Collaborative and innovative virtual team culture.
• Generous annual leave and paid time off.