Senior Data Scientist

1 week ago

kochi, India beBeeData Full time

Machine Learning Engineer PositionWe are seeking a skilled Machine Learning Engineer to join our team. This role involves designing, building, and operating end-to-end data pipelines using batch and streaming ETL/ELT processes.Create robust data pipelines that handle large volumes of data from various sources, including positions, trades, prices, fundamentals, benchmarks, corporate actions, CRM, and clickstream.Develop and maintain offline and online feature platforms for client risk signals, asset factors, churn and upsell predictions, RIA productivity analysis, and other business-critical features.Implement model training and MLOps processes to productionize forecasting models, personalization algorithms, anomaly detection systems, NLP document processing tools, and other machine learning applications.Ensure data quality and governance by implementing contracts, unit tests, expectations, schema registries, lineage tracking, catalogues, PII tokenization, and role-based access control.Ex expose model and feature services through low-latency endpoints and scalable infrastructure, partnering with backend teams to integrate into advisor and client applications.Cross-functional collaboration is key in this role, working closely with Quant/Research, Product, and Compliance teams to translate business problems into robust data-driven solutions and measurable outcomes.Requirements:A minimum of 5 years of experience in data engineering or ML engineering, including owning production pipelines and shipping machine learning models into production.Expertise in Python programming and one of SQL or Scala; strong skills in Spark/PySpark and a workflow orchestrator such as Airflow, Prefect, or Dagster.Familiarity with cloud data stacks, specifically AWS (S3, Glue, EMR, Lambda/ECS/EKS, Step Functions) or GCP/Azure equivalents, and DBT or similar data transformation tools.Experience in building streaming systems using Kafka/Kinesis/PubSub, with a solid understanding of event schemas and backfills.MLOps expertise in feature stores (Feast/Tecton), experiment tracking (MLflow/W&B), model registries, and model serving (SageMaker, Vertex AI, Triton, Ray Serve, or KFServing).Production monitoring skills, including data drift detection, model performance evaluation, alerting (Prometheus/Grafana, CloudWatch, Datadog).Understanding of security fundamentals, including IAM, VPC segmentation, KMS, secrets management, least-privilege access, and PII handling.Clear communication skills to turn ambiguous business requirements into actionable roadmaps and deliverables.

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