About DKatalis
DKatalis is a financial technology company with multiple offices in the APAC region. We serve as the technological backbone for Bank Jago, an Indonesian digital bank whose mission is to drive financial inclusion by developing bespoke banking products and services that cater to a diverse customer base, from individual retail consumers to micro, small, and medium enterprises.
What sets us apart?
- Real-world Impact: Your models and analyses will directly shape bespoke banking products, influencing financial services for millions of users across Indonesia.
- Data-Rich Environment: Access vast, diverse datasets spanning individual retail consumers to micro, small, and medium enterprises (MSMEs).
- Cutting-edge Tech Stack: Leverage the latest in big data technologies, cloud computing, and machine learning frameworks.
Cross-functional Collaboration: Work closely with product, engineering, and business teams to turn data insights into actionable strategies.
About the Role
As a part of our Data Science team, you'll play a crucial role in building, deploying, and supporting our digital products. Your focus will be on creating robust and resilient systems for Data Science use cases, catering not only to our products but also supporting various business units of Bank Jago, including Go-to-Market (GTM), Risk, and Servicing. You'll collaborate closely with Engineers, Product, and business stakeholders, collectively ensuring that our products maintain a high standard of quality. This role offers the opportunity to witness your impact on the daily lives of Bank Jago's customers while enjoying a dynamic and collaborative work environmentAbout the role
We are seeking an experienced Machine Learning Engineer to collaborate with our data science team in rapidly designing, building, training, optimizing, deploying, and monitoring models in production environments. While our data scientists focus on data analysis, feature engineering, and model design, you will be responsible for providing and managing the infrastructure and tooling to ensure reliable deployment and operation in production.
Key Responsibilities:
- Build and operate the infrastructure to enable full-cycle Machine Learning Ops.
- Implement and manage feature stores, addressing challenges such as training-serving skew.
- Develop models supporting both online and offline inference, integrated into customer-facing product features requiring 99.9% uptime.
- Work closely with data engineering, product, software engineering, and DevOps teams.
- Balance technical and business concerns pragmatically, respecting long-term business objectives.
Your work will support various business capabilities, including:
- Digital banking product features (e.g., smart financial recommendations)
- Business operations optimization
- Growth, go-to-market, and customer engagement strategies
- Fraud detection and risk management functions
- Improving efficiency of technical operations within the business
Requirements:
Education and Background
- Bachelor's or Master's degree in Computer Science, Information Systems, or Engineering with a strong background in mathematics and statistics.
Technical Skills and Experience
- 3-5+ years of software engineering experience, with at least 2 years deploying machine learning models in production environments at scale.
- Familiarity with tools in the PyData ecosystem (Numpy, Scipy, pandas, scikit-learn, PyTorch, TensorFlow).
- Extensive experience with cloud technologies, particularly GCP (VertexAI, BigQuery).
- Proficiency with Kubernetes and container orchestration.
- Experience with Kubeflow or similar ML-specific orchestration tools.
- Experience with big data / distributed processing systems.
Knowledge and Expertise
- Demonstrated understanding of theoretical foundations underpinning machine learning and deep learning models, coupled with extensive hands-on experience solving real-world problems.
- Strong understanding of ML model monitoring, versioning, and A/B testing in production environments.
Soft Skills
- Excellent communication skills, both written and verbal.
Preferred Qualifications:
- Experience in the retail banking sector.
- Contributions to open-source ML or data engineering projects.
- Experience mentoring junior engineers or data scientists.
- Proven track record of improving ML infrastructure efficiency and scalability.
Stand-out Qualities
- Strong portfolio of relevant projects (e.g., well-maintained GitHub repositories)
- Active participation in the data science community (giving talks, attending meetups)
- Demonstrated thought leadership (blog posts, articles, or contributions to open-source projects)
- Self-motivated with a track record of initiating and completing projects
The ideal candidate will bring a wealth of experience in MLOps, demonstrating not just technical proficiency but also the ability to drive best practices, mentor team members, and contribute to the strategic direction of our ML infrastructure. We're looking for individuals who are not only technically proficient but also engaged with the broader data science community and capable of driving innovation within our team.
What Will You Do
- Collaborate cross-functionally to design, develop, and enhance our digital products
- Shape superior product experiences by refining platforms, services, and tools
- Engage closely with diverse teams, learning effective communication across departments and cultures
- Assist in ensuring application performance, security, and responsiveness
- Continuously improve code quality, streamline processes, and prioritise customer satisfaction
What You Need To Have
- 5 to 7 years of professional experience (Machine Learning / Data Engineering / Software Engineering background are welcome to apply)
- Proficiency in Python
- Experience with Agile and Lean/XP practices
- Strong communication in distributed, multicultural teams
- Passion for end-to-end experiences and software architecture
- Balance between quality and timely delivery
- Adaptability, problem-solving skills, and a positive attitude
Desired Bonus Skills
- Innovation Showcase - Highlight personal side projects or unique contributions demonstrating innovative thinking
- Machine Learning Proficiency - Knowledge in machine learning concepts, algorithms, and frameworks (e.g., TensorFlow, PyTorch) is advantageous
- Data Engineering Competence - Familiarity with data engineering practices (ETL processes, database design, Apache Spark) is a plus
