Data Engineer Java/Scala/Python

Job description

<p><p><b>About the job</b><br/><br/><b>Position Overview</b><br/><br/>Job Title : Software Development Engineer 2<br/><br/>Department : Technology<br/><br/>Location : Bangalore, India<br/><br/>Reporting To : Senior Research Manager - Data<br/><br/><b>Position Purpose</b><br/><br/>The Research Engineer Data will play a pivotal role in advancing TookiTakis AI-driven compliance and financial crime prevention platforms through applied research, experimentation, and data innovation.<br/><br/> This role is ideal for professionals who thrive at the intersection of research and engineering, turning cutting-edge data science concepts into production-ready capabilities that enhance TookiTakis competitive edge in fraud prevention, AML compliance, and data intelligence.<br/><br/>The role exists to bridge research and engineering by<br/><br/>- Designing and executing experiments on large, complex datasets<br/><br/>- Prototyping new data-driven algorithms for financial crime detection and compliance automation.<br/><br/>- Collaborating across product, data science, and engineering teams to transition research outcomes into scalable, real-world solutions.<br/><br/>- Ensuring the robustness, fairness, and explainability of AI models within TookiTakis compliance platform.<br/><br/><b>Key Responsibilities : </b><br/><br/><b>Applied Research & Prototyping : </b><br/><br/>- Conduct literature reviews and competitive analysis to identify innovative approaches for data processing, analytics, and model developments.<br/><br/>- Build experimental frameworks to test hypotheses using real-world financial datase<br/><br/>- Prototype algorithms in areas such as anomaly detection, graph-based analytics, and natural language processing for compliance workflows.<br/><br/><b>Data Engineering for Research : </b><br/><br/>- Develop data ingestion, transformation, and exploration pipelines to support experimentation.<br/><br/>- Work with structured, semi-structured, and unstructured datasets at scale.<br/><br/>- Ensure reproducibility and traceability of experiments<br/><br/><b>Algorithm Evaluation & Optimization : </b><br/><br/>- Evaluate research prototypes using statistical, ML, and domain-specific metrics.<br/><br/>- Optimize algorithms for accuracy, latency, and scalability.<br/><br/>- Conduct robustness, fairness, and bias evaluations on mode.<br/><br/><b>Collaboration & Integration : </b><br/><br/>- Partner with data scientists to transition validated research outcomes into production-ready to code.<br/><br/>- Work closely with product managers to align research priorities with business goals<br/><br/>- Collaborate with cloud engineering teams to deploy research pipelines in hybrid environments.<br/><br/><b>Documentation & Knowledge Sharing : </b><br/><br/>- Document experimental designs, results, and lessons learned<br/><br/>- Share best practices across engineering and data science teams to accelerate innovation<br/><br/><b>Qualifications and Skills :</b><br/><br/>- Education Required : Bachelors degree in Computer Science, Data Science, Applied Mathematics, or related field<br/><br/>- Preferred : Masters or PhD in Machine Learning, Data Engineering, or a related research intensive field<br/><br/><b>Experience :</b><br/><br/>- Minimum 4 - 7 years in data-centric engineering or applied research roles.<br/><br/>- Proven track record of developing and validating algorithms for large-scale data processing or machine learning applications.<br/><br/>- Experience in financial services, compliance, or fraud detection is a strong plus.<br/><br/><b>Technical Expertise : </b><br/><br/>- Programming : Proficiency in Scala, Java, or Python<br/><br/>- Data Processing : Experience with Spark, Hadoop, and Flink<br/><br/>- ML/Research Frameworks : Hands-on with TensorFlow, PyTorch, or Scikit-learn<br/><br/>- Databases : Experience with both relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Cassandra, ElasticSearch).<br/><br/>- Cloud Platforms : Experience with AWS (preferred) or GCP for research and data pipelines.<br/><br/>- Tools : Familiarity with experiment tracking tools like MLflow or Weights & Biases.<br/><br/>- Application Deployment : Strong experience with CI/CD practices, Containerized Deployments through Kubernetes, Docker Etc.<br/><br/>- Streaming frameworks : Strong experience in creating highly performant and scalable real time streaming applications with Kafka at the core<br/><br/>- Data Lakehouse : Experience with one of the modern data lakehouse platforms/formats such as Apache Hudi, Iceberg, Paimon is a very strong Plus.<br/><br/><b>Soft Skills : </b><br/><br/>- Strong analytical and problem-solving abilities.<br/><br/>- Clear concise communication skills for cross-functional collaboration.<br/><br/>- Adaptability in fast-paced, evolving environments.<br/><br/>- Curiosity-driven with a bias towards experimentation and iteration.<br/><br/><b>Key Competencies : </b><br/><br/>- Innovation Mindset : Ability to explore and test novel approaches that push boundaries in data analytics.<br/><br/>- Collaboration : Works effectively with researchers, engineers, and business stakeholders.<br/><br/>- Technical Depth : Strong grasp of advanced algorithms and data engineering principles.<br/><br/>- Problem Solving : Dives deep into the logs, metrics and code and identifying problems opportunities for performance tuning and optimization.<br/><br/>- Ownership : Drives research projects from concept to prototype to production.<br/><br/>- Adaptability : Thrives in ambiguity and rapidly changing priorities.<br/><br/>- Preferred Certifications in AWS Big Data, Apache Spark, or similar technologies.<br/><br/>- Experience in compliance or financial services domains.<br/><br/><b>Success Metrics : </b></p><p><br/></p><p>- Research to Production Conversion : % of validated research projects integrated into TookiTakis platform<br/><br/>- Model Performance Gains : Documented improvements in accuracy, speed, or robustness from research initiatives.<br/><br/>- Efficiency of Research Pipelines : Reduced time from ideation to prototype completion.<br/><br/>- Collaboration Impact : Positive feedback from cross-functional teams on research Competitive Salary : Aligned with industry standards and experience.<br/><br/>- Professional Development : Access to training in big data, cloud computing, and data integration tools.<br/><br/>- Comprehensive Benefits : Health insurance and flexible working options.<br/><br/>- Growth Opportunities : Career progression within Tookitakis rapidly expanding Services Delivery team</p><br/></p> (ref:hirist.tech)

Required Skill Profession

Computer Occupations