About Us:
As a Data Engineer at Kenility, you’ll join a tight-knit family of creative developers, engineers, and designers who strive to develop and deliver the highest quality products into the market.
Technical Requirements:
- Bachelor’s degree in Computer Science, Software Engineering, or a related field.
- More than seven years of professional background in data science, applied machine learning, or similar quantitative disciplines.
- Solid expertise in statistics and experimentation, including hypothesis testing, causal inference, and sound evaluation methodologies.
- Demonstrated success in developing and deploying predictive models across use cases such as classification, regression, and time series forecasting, with a focus on measurable business results.
- Advanced command of Python and SQL, along with confidence working in production-grade data pipelines and workflows.
- Experience establishing meaningful success metrics, aligning expectations with stakeholders, and delivering complete solutions from concept to implementation.
- Strong written communication abilities combined with a practical mindset suited to dynamic and evolving environments.
- Hands-on experience managing model performance, identifying data or concept drift, and strengthening feature stability and reliability.
- Exposure to domains such as credit risk, underwriting, fraud detection, risk signals, or financial forecasting is valued.
- Familiarity with modern data platforms and warehouses, including tools such as BigQuery or Snowflake, as well as transformation frameworks like dbt, is a plus.
- Knowledge of MLOps practices, such as deployment workflows, monitoring strategies, feature stores, orchestration, and cloud-based environments, is desirable.
- Experience working with complex external data sources, including banking information, eCommerce platforms, or marketing-related signals, is considered an advantage.
- Minimum Upper Intermediate English (B2) or Proficient (C1).
Tasks and Responsibilities:
- Design and run data science initiatives, including causal inference studies, A/B testing, and offline model evaluation.
- Build, assess, and continuously refine predictive models for areas such as credit and risk scoring, revenue projections, and policy effectiveness.
- Take ownership of model health by defining performance indicators, detecting drift, and improving data quality and feature robustness.
- Collaborate closely with Product Engineering teams to bring models and analytical solutions into production with a strong focus on stability, reproducibility, and long-term maintainability.
- Conduct exploratory analysis, engineer relevant features, and apply rigorous validation approaches to complex and imperfect real-world datasets.
- Present findings, recommendations, and analytical outcomes clearly to both technical and non-technical audiences through presentations and written documentation.
- Contribute to higher technical standards by enhancing analytical practices, code review processes, and documentation quality.
- Support the growth of other team members through mentoring, collaborative work sessions, constructive feedback, and knowledge sharing.
Soft Skills:
- Responsibility
- Proactivity
- Flexibility
- Great communication skills
