We are hiring an Applied Data Scientist to build the models that turn raw transaction data into the insights our banks’ customers see every day.
The stack is deliberately wide: classical ML on tabular data, deep learning, LLM-based systems, and agentic flows. We choose the right approach for each problem, balancing analytical quality, production readiness, and time to market. You drive the analytical work, working closely with Product and Engineering to take ideas from evidence through production.
Responsibilities- Analyze and interpret complex datasets to identify trends and patterns.
- Collaborate with cross-functional teams to develop data-driven solutions.
- Build and maintain predictive models to enhance business decisions.
- Communicate findings and insights effectively to stakeholders.
- Stay updated with the latest data science technologies and methodologies.
- Contribute to the continuous improvement of data processes and systems.
- Bachelor's degree in Data Science, Computer Science, or related field.
- 3+ years of experience in applied data science or a similar role.
- Proven experience with machine learning algorithms and statistical modeling.
- Proficiency in programming languages such as Python or R.
- Strong analytical skills and ability to work with large datasets.
- Excellent communication and teamwork skills.
- Experience with data visualization tools like Tableau or Power BI.
- FinTech or banking experience.
- Transformer models — fine-tuning and production inference.
- Experience taking a model into production, including monitoring and post-launch iteration.
- Cloud platforms (AWS, Azure).
Skills Required
- 2–3 years of hands-on experience as a Data Scientist in a product environment
- Strong Python skills, including Pandas, NumPy, scikit-learn, and PyTorch
- Experience with classical machine learning on tabular data, including feature engineering, gradient-boosted trees, and model evaluation
- Hands-on experience building GenAI or LLM applications, including RAG, prompt engineering, and evaluation of LLM-based systems
- Experience building agentic systems involving tool use, multi-step workflows, and orchestration
- Clear technical writing and ability to explain models to non-technical audiences
- FinTech or banking experience
- Experience with transformer model fine-tuning and production inference
- Experience taking models into production, including monitoring and post-launch iteration
- Experience with cloud platforms such as AWS or Azure
What We Do
Money Management & PFM for Banks & Financial Institutions. Personetics serves hundreds of banks and financial institutions across 30 global markets. See how your bank can harness the power of AI to engage customers and increase revenue with financial data-driven personalized customer engagement, PFM, and money management platform. Harnessing the power of AI, Personetics’ Self-Driving Finance™ solutions are used by the world’s largest financial institutions to transform digital banking into the center of the customer’s financial life – providing real-time personalized insight and advice, automating financial decisions, and simplifying day-to-day money management. Serving over 135 million bank customers worldwide, Personetics has the largest direct customer impact of any AI solution provider in banking today. Personetics now counts among its customers 6 of the top 12 banks in North America and Europe, as well as other leading banks throughout the world. Personetics is the global leader in financial-data-driven personalized banking and customer engagement for financial services and the company behind the industry’s first Self-Driving Finance™ platform. Led by a team of seasoned FinTech entrepreneurs with a proven track record, Personetics is a rapidly growing company with offices in New York, London, Paris, Singapore, Tokyo, Tel Aviv and Nazareth. The company has been named a Gartner Cool Vendor, a Top Ten FinTech Company by KPMG, and a Top Ten Company to Watch by American Banker
