About the company

Geekhunter is hiring on behalf of our client, an Independent Software Provider offering comprehensive end-to-end solutions tailored for financial businesses.

Job Responsibilities

As the Data Analytics Manager, you will lead the development of predictive models that drive strategic decision-making in areas such as credit risk, collections, fraud prevention, and broader business optimization within the financial services sector. You will be responsible for the full lifecycle of model development, from design to deployment, while collaborating with stakeholders to turn complex data into actionable insights.
Build and deploy predictive models for credit risk assessment, collection strategies, fraud detection, and other financial business applications.
Manage the full end-to-end lifecycle of predictive models—including design, development, validation, deployment, and ongoing performance monitoring.
Engineer advanced predictive features from internal datasets and evaluate external data sources to enrich customer profiles.
Translate complex business problems into robust analytical model frameworks.
Communicate insights clearly and effectively to various stakeholders, align teams with the analytics strategy, and drive buy-in across departments for data initiatives

Job Requirements

Bachelor’s degree (or equivalent) in a quantitative discipline such as Statistics, Mathematics, Computer Science, or Engineering.
Minimum 4 years of hands-on experience developing and validating machine learning models, particularly in collection risk or credit scoring for financial products in Multifinance / BNLP (Buy Now Pay Later).
At least 2 years of experience in a leadership role managing analytics or data science teams.
Strong understanding of mathematical and statistical principles.
Proficient in machine learning techniques such as bagging, boosting, and recommendation systems.
Expertise in feature engineering across diverse data types (structured and unstructured).
Proven track record in developing and deploying machine learning models in production environments.
Ability to balance model performance with business practicality and constraints.
Strong business acumen with the ability to translate business problems into data science solutions.
Excellent communication skills with the ability to work closely with non-technical stakeholders.
Experience across multiple analytics domains (e.g., risk, marketing, fraud) is a plus.

Benefits