About brighthush

brighthush is more than a fintech company; it is a global movement dedicated to reshaping the financial landscape for millions of underserved individuals. Our core belief is that data, when harnessed responsibly, can unlock opportunities for those historically excluded from mainstream financial services. By blending cutting‑edge machine learning, cloud‑native architecture, and open‑source tools, we design credit and savings products that are not only innovative but also accessible, fair, and sustainable. At brighthush, data is the engine that powers smarter, more equitable financial solutions, and we are committed to using that engine to drive real‑world impact.

Why brighthush?

When you become part of brighthush, you step into a purpose‑driven organization that values curiosity, collaboration, and a relentless pursuit of excellence. Our culture encourages experimentation, continuous learning, and the freedom to iterate on ideas that matter. Whether you are a seasoned data scientist or an emerging talent, you will find a supportive environment that nurtures growth, celebrates diversity, and rewards innovation. We believe that the best solutions come from teams that listen, learn, and act together.

Role Overview

As a Senior Customer Data Scientist at brighthush, you will lead the development of sophisticated predictive models that inform our customer acquisition, risk assessment, and product optimization strategies. You will partner closely with product managers, engineers, and business analysts to translate complex data insights into actionable business decisions. Your work will directly shape how millions of customers access credit and savings, making a tangible difference in their financial lives and advancing our mission of financial inclusion.

Key Responsibilities

• Model Development & Deployment: Design, build, and validate machine learning models using Python, sklearn, h2o, PyTorch, and TensorFlow. Ensure models are production‑ready, scalable, and maintainable across cloud environments.
• Data Engineering & Analysis: Extract, transform, and load (ETL) large datasets from SQL databases and data lakes. Conduct exploratory data analysis to uncover patterns, trends, and anomalies that inform feature engineering and model selection.
• Stakeholder Collaboration: Translate technical findings into clear, concise insights for non‑technical audiences. Present results to senior leadership, product teams, and external partners, guiding strategic decisions and product roadmaps.
• Model Governance & Documentation: Maintain rigorous documentation of model assumptions, performance metrics, and version control using Git and MLflow. Ensure compliance with internal data governance policies and regulatory standards such as GDPR and local data protection laws.
• Continuous Improvement: Monitor model performance in production, identify drift, and iterate on model improvements. Stay abreast of emerging techniques and industry best practices, and incorporate them into our analytics pipeline.
• Mentorship & Knowledge Sharing: Coach junior data scientists and analysts. Lead workshops, brown‑bag sessions, and knowledge‑sharing initiatives to elevate the overall analytical capability of the team.
• Experimentation & Validation: Design and run A/B tests and controlled experiments to validate model hypotheses and measure business impact. Collaborate with the experimentation platform team to ensure robust statistical rigor.
• Cross‑Functional Leadership: Act as a bridge between data science, engineering, product, and compliance teams, ensuring that data‑driven solutions align with business objectives and regulatory requirements.
• Innovation & Thought Leadership: Identify opportunities to apply emerging AI/ML techniques—such as graph neural networks, reinforcement learning, or federated learning—to solve new business challenges. Publish internal whitepapers and external blog posts to position brighthush as a thought leader in data‑driven financial inclusion.

Essential Qualifications

• Academic Foundation: Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field. A Master’s or Ph.D. is highly desirable.
• Professional Experience: 3–5 years of data science experience, preferably in fintech, financial services, or a similarly data‑driven industry. Experience with credit risk modeling or customer segmentation is a plus.
• Technical Proficiency: Advanced SQL skills for data extraction and manipulation; strong programming in Python with hands‑on experience using pandas, numpy, sklearn, h2o, PyTorch, and optionally TensorFlow.
• Version Control: Proficiency with Git for code management, collaboration, and reproducibility.
• Communication: Excellent written and verbal communication skills, enabling clear articulation of complex concepts to diverse audiences, including senior leadership and non‑technical stakeholders.
• Business Acumen: Ability to translate business problems into data‑driven solutions that align with strategic objectives and deliver measurable impact.
• Problem‑Solving Mindset: Strong analytical and critical thinking skills, with a proven track record of solving ambiguous problems and delivering actionable insights.
• Ethical Data Stewardship: Demonstrated commitment to responsible AI practices, data privacy, and fairness in algorithmic decision‑making.

Preferred Qualifications

• Master’s degree or higher in a quantitative discipline.
• Experience with cloud platforms and data pipeline orchestration tools such as Airflow or dbt.
• Knowledge of regulatory frameworks affecting fintech, such as GDPR, PSD2, or local data protection laws.
• Familiarity with A/B testing frameworks and experimentation platforms.
• Prior experience working in a remote, distributed team environment.
• Published research or contributions to open‑source data science projects.
• Experience with model monitoring tools like Evidently, Prometheus, or Grafana.
• Exposure to advanced feature stores or model serving platforms (e.g., Feast, TFX).

Technical Skills & Competencies

• Data Manipulation & Analysis: SQL, Python (pandas, numpy), data visualization libraries (matplotlib, seaborn, plotly).
• Machine Learning & Deep Learning: sklearn, h2o, PyTorch, TensorFlow (optional).
• Model Deployment & Monitoring: Docker, Kubernetes, MLflow, Airflow, Evidently.
• Version Control & Collaboration: Git, code hosting platforms, CI/CD pipelines.
• Statistical Analysis: hypothesis testing, confidence intervals, Bayesian methods, causal inference.
• Business Acumen: understanding of credit risk, customer segmentation, product lifecycle, and financial metrics.
• Soft Skills: problem‑solving, critical thinking, adaptability, teamwork, and mentorship.

Career Growth & Learning Opportunities

At brighthush, we invest heavily in the professional development of our people. You will have access to:

• Mentorship programs pairing you with senior leaders in data science, product strategy, and compliance.
• Annual learning stipend for courses, certifications, and conferences, including data science bootcamps, cloud certifications, and industry meet‑ups.
• Internal hackathons, innovation challenges, and data‑science competitions that encourage experimentation and creativity.
• Clear career pathways—from Senior Data Scientist to Lead Data Scientist, Head of Analytics, or Chief Data Officer—supported by regular performance reviews and skill assessments.
• Opportunities to contribute to open‑source projects, speak at industry panels, and publish research that elevates the brand of brighthush and the wider data‑science community.
• Access to premium data‑science tools and platforms, including cloud credits, GPU instances, and advanced analytics libraries.
• Cross‑functional rotational programs that allow you to experience product management, engineering, and compliance, broadening your skill set and career perspective.

Work Environment & Culture

brighthush thrives on a culture of transparency, inclusivity, and continuous improvement. As a remote‑first organization based in South Africa, we offer:

• Flexible working hours to accommodate different time zones and personal commitments.
• Fully equipped home‑office stipend to ensure you have the tools you need.
• Regular virtual team‑building events, knowledge‑sharing sessions, and wellness programs.
• A collaborative environment where cross‑functional teams co‑create solutions and celebrate successes together.
• Commitment to diversity, equity, and inclusion—ensuring every team member feels valued and empowered.
• Annual company retreats and off‑site workshops that foster team cohesion and strategic alignment.
• Transparent decision‑making processes, with open forums for feedback and idea submission.
• Recognition programs that celebrate both individual and team achievements.

Compensation & Benefits

While specific figures vary based on experience and location, brighthush offers a competitive total rewards package that includes:

• Base salary aligned with market standards for senior data science roles in South Africa.
• Performance‑based bonuses tied to individual and company milestones.
• Equity options to share in the company’s growth and success.
• Health and wellness benefits, including medical, dental, and mental health coverage.
• Retirement savings plan with company matching.
• Generous paid time off, parental leave, and flexible vacation policies.
• Continuous learning budget and access to premium data‑science tools and platforms.
• Annual stipend for professional development, including conferences, workshops, and certifications.
• Employee assistance program and mental health resources.
• Regular wellness challenges and virtual fitness classes.

How to Apply

Ready to make a real impact on financial inclusion? Submit your application through our careers portal and join a team that values data, innovation, and people. We look forward to exploring how your expertise can help brighthush shape the future of finance.