Senior Data Science Engineer
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Job Description
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.
Key Responsibilities
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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.
Key Skills & Experience
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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.
Why Join Us
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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.
Employment Type
Full Time
