Senior Data Science Engineer
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Job Summary
We are seeking a Senior Data Science Engineer with expertise in building and operationalizing production-grade machine learning pipelines. The ideal candidate will have hands-on experience with MLFlow Databricks and Azure ML and a strong background in developing ML solutions for healthcare predictive use cases. This role will be central to embedding ML models into enterprise data workflows and ensuring their ongoing performance.
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ML Pipeline Engineering: Design build and maintain scalable ML pipelines leveraging MLFlow Databricks and Azure ML.
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Model Development: Develop and optimize ML models particularly for healthcare predictive analytics.
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Integration: Embed ML models into KPI-driven data processing and analytics workflows.
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Model Lifecycle Management: Implement model monitoring drift detection retraining and continuous improvement strategies.
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Collaboration: Work closely with data engineers data scientists and business stakeholders to deploy solutions that deliver measurable impact.
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6 years of experience in machine learning engineering or applied data science.
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Hands-on expertise with MLFlow Databricks and Azure ML.
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Strong experience in developing and deploying predictive ML models ideally within healthcare.
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Proficiency in Python SQL PySpark and modern ML frameworks (TensorFlow PyTorch or Scikit-learn).
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Knowledge of model monitoring drift detection and automated retraining strategies.
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Familiarity with cloud-native ML architectures and CI/CD for ML (MLOps).
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Strong problem-solving and collaboration skills with a focus on production-grade delivery.
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Work on cutting-edge healthcare predictive analytics solutions.
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Gain exposure to enterprise-scale ML pipelines on modern platforms.
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Opportunity to drive end-to-end ML lifecycle ownership.
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Competitive compensation and career growth in a fast-growing data-driven organization.
Key Skills
- Apache Hive
- S3
- Hadoop
- Redshift
- Spark
- AWS
- Apache Pig
- NoSQL
- Big Data
- Data Warehouse
- Kafka
- Scala
