Data Innovation Engineer

Research Data Engineer Position Overview

We are seeking a skilled Research Data Engineer to play a pivotal role in advancing our AI-driven compliance and financial crime prevention platforms through applied research, experimentation, and data innovation.

This is an ideal opportunity for professionals who thrive at the intersection of research and engineering, turning cutting-edge data science concepts into production-ready capabilities that enhance our competitive edge in fraud prevention, AML compliance, and data intelligence.

The successful candidate will bridge research and engineering by designing and executing experiments on large, complex datasets, prototyping new data-driven algorithms for financial crime detection and compliance automation, collaborating across product, data science, and engineering teams to transition research outcomes into scalable, real-world solutions, and ensuring the robustness, fairness, and explainability of AI models within our compliance platform.

Key Responsibilities:

• Conduct literature reviews and competitive analysis to identify innovative approaches for data processing, analytics, and model developments.
• Build experimental frameworks to test hypotheses using real-world financial datasets.
• Prototype algorithms in areas such as anomaly detection, graph-based analytics, and natural language processing for compliance workflows.

Data Engineering for Research:

• Develop data ingestion, transformation, and exploration pipelines to support experimentation.
• Work with structured, semi-structured, and unstructured datasets at scale.
• Ensure reproducibility and traceability of experiments.

Algorithm Evaluation & Optimization:

• Evaluate research prototypes using statistical, ML, and domain-specific metrics.
• Optimize algorithms for accuracy, latency, and scalability.
• Conduct robustness, fairness, and bias evaluations on models.

Collaboration & Integration:

• Partner with data scientists to transition validated research outcomes into production-ready code.
• Work closely with product managers to align research priorities with business goals.
• Collaborate with cloud engineering teams to deploy research pipelines in hybrid environments.

Documentation & Knowledge Sharing:

• Document experimental designs, results, and lessons learned.
• Share best practices across engineering and data science teams to accelerate innovation.

Qualifications and Skills:

• Bachelor's degree in Computer Science, Data Science, Applied Mathematics, or related field.
• Minimum 4–7 years in data-centric engineering or applied research roles.
• Proven track record of developing and validating algorithms for large-scale data processing or machine learning applications.
• Experience in financial services, compliance, or fraud detection is a strong plus.

Technical Expertise:

• Programming: Proficiency in Scala, Java, or Python.
• Data Processing: Experience with Spark, Hadoop, and Flink.
• ML/Research Frameworks: Hands-on with TensorFlow, PyTorch, or Scikit-learn.
• Databases: Experience with both relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Cassandra, ElasticSearch).
• Cloud Platforms: Experience with AWS (preferred) or GCP for research and data pipelines.
• Tools: Familiarity with experiment tracking tools like MLflow or Weights & Biases.
• Application Deployment: Strong experience with CI/CD practices, Containerized Deployments through Kubernetes, Docker Etc.
• Streaming frameworks: Strong experience in creating highly performant and scalable real-time streaming applications with Kafka at the core.
• Data Lakehouse: Experience with one of the modern data lakehouse platforms/formats such as Apache Hudi, Iceberg, Paimon is a very strong Plus.

Soft Skills:

• Strong analytical and problem-solving abilities.
• Clear concise communication skills for cross-functional collaboration.
• Adaptability in fast-paced, evolving environments.
• Curiosity-driven with a bias towards experimentation and iteration.

Benefits:

• Competitive salary aligned with industry standards and experience.
• Professional development opportunities, including training in big data, cloud computing, and data integration tools.
• Comprehensive benefits package, including health insurance and flexible working options.
• Growth opportunities for career progression within our rapidly expanding Services Delivery team.

Success Metrics:

• Research to Production Conversion: % of validated research projects integrated into our platform.
• Model Performance Gains: Documented improvements in accuracy, speed, or robustness from research initiatives.
• Efficiency of Research Pipelines: Reduced time from ideation to prototype completion.
• Collaboration Impact: Positive feedback from cross-functional teams on research integration.

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