Senior Associate
1 month ago
Job Responsibilities: Axtria is a global provider of cloud software and data analytics to the life sciences industry. We help life sciences companies transform the product commercialization journey to drive sales growth and improve healthcare outcomes for patients. We are seeking high energy, driven and innovative ML Ops Engineers to join our Data Science Practice to develop new, specialized capabilities for Axtria, and to accelerate the company’s growth supporting our clients’ commercial & clinical strategies.
- Craft winning proposals to grow the Data Science Practice.
- Develop and operationalize scalable processes to deliver on large & complex client engagements.
- Ensure profitable delivery and great customer experience – design, deploy, operate, and maintain scalable and efficient automated machine learning systems. Monitor and improve performance while accounting for information security, access, model governance, data & model drift, etc.
- Build an A team – hire the required skills sets and nurture them in a supporting environment to develop strong delivery teams for the Data Science Practice.
- Train and mentor staff and establish best practices and ways of working to enhance ML ops capabilities at Axtria. Operationalize an eco-system for continuous learning & development.
- Write white papers, collaborate with academia and participate in relevant forums to continuously upgrade self knowledge & establish Axtria’s thought leadership in this space.
Qualifications & Experience:
- 3+ years of experience in the data science space – preferably with the end to end automated ecosystem including, but not limited to, building data pipelines, developing & deploying scalable models, orchestration, scheduling, automation, ML operations.
- Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS, MS Azure or GCP)
- Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes, OpenShift.
- Programming languages like Python, Go, Ruby or Bash, good understanding of Linux, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc.
- Ability to understand tools used by data scientist and experience with software development and test automation.
- Good understanding of advanced AI/ML algorithms & their applications
- Applications and Digital Automation Solutions, Low Code /No Code and Automation Platforms, designing API's and exposure to DevOps, React/Angular, containers, building visualization layer.
- Knowledge of self-service analytics platforms such as Dataiku/ KNIME/ Alteryx will be an added advantage. MS Excel knowledge is mandatory. Familiarity with Life Sciences is a plus
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