Machine Learning Engineer
2 weeks ago
Machine Learning Engineer
About us:
Why Norstella? Norstella unites market-leading companies that all have a shared goal of improving patient access. Each organization (Evaluate, Citeline, MMIT, Panalgo, The Dedham Group) delivers must-have answers for critical strategic and commercial decision-making.
Together, we help our clients:
- Assess the market need and competitive landscape
- Know precisely which drugs to prioritize in their portfolios
- Find out where the launch difficulties will be—before they’re difficulties
- Track and improve market access post-launch
By combining the efforts of each organization under Norstella, we can offer an even wider breadth of expertise, cutting-edge data solutions and expert advisory services alongside advanced technologies such as real-world data, machine learning-driven predictive analytics. At Norstella, we don’t just deliver information and insights. We deliver answers you can act on.
Job Description
About the role:
As a Machine Learning Engineer at Norstella, you will be a key player in a cross-functional team of data scientists, analysts, and DevOps engineers across our Strategic Intelligence, Clinical, Consulting, and Content departments. You will support our data scientists, helping them operationalize machine learning models and develop high-quality, scalable API services and/or batch processes. Your work will play a critical role in our efforts to deliver predictive analytics for streamlining and informing clinical trial planning, drug development portfolio management, and drug launch strategies.
Responsibilities:
- Collaborate with data scientists and engineers to develop and deploy machine learning models.
- Build and maintain scalable machine learning solutions in production.
- Create secure AWS SageMaker Endpoints and Lambdas to operationalize machine learning models.
- Develop and maintain secure, robust, and scalable data pipelines.
- Implement MLOps best practices regarding data and model drifts checks to ensure data quality and model accuracy and reliability via appropriate monitoring and troubleshooting.
- Integrate MLOps checks and stages (e.g., automated model deployment following successful re-training) within the CI/CD pipeline.
- Collaborate with other teams to define request and response formats for services and define the appropriate AWS service to use, depending on the use case.
- And other duties as assigned
Qualifications:
The skills you bring to the table:
- Bachelor’s degree in Computer Science, STEM, or a related field. Equivalent professional experience will also be considered.
- At least 3 years of professional experience in Machine Learning Engineering, with a focus on deploying secure and robust models in production.
- Strong programming skills in Python, with experience in libraries such as scikit-learn, pandas, scipy, click, and flask and/or FastAPI.
- Experience with the AWS ecosystem, specifically with services like SageMaker and Lambda.
- Experience working in a cross-functional team in a professional setting.
- Good understanding of software development lifecycle and practices, including Git and version control, code reviews, and functional, unit and integration testing.
- Excellent problem-solving skills and the ability to work independently.
Bonus points if you have experience in:
- Experience managing the lifecycle of a machine learning model, including updates, tuning and retraining.
- Familiarity with AWS services beyond SageMaker and Lambda, such as S3, EC2, API Gateway, ECR, and DynamoDB.
- Experience working with large and complex data sets, including data cleaning and preprocessing.
- Knowledge of advanced machine learning techniques (neural networks, ensemble learning, reinforcement learning, etc.) and the ability to implement them in Python.
- Experience with Docker or other containerization technologies.
- Familiarity with CI/CD processes, especially as applied to ML operations (MLOps).
- Background or interest in pharmaceutical data, life sciences, or healthcare.
The guiding principles for success at Norstella:
01: Bold, Passionate, Mission-First
We have a lofty mission to Smooth Access to Life Saving Therapies and we will get there by being bold and passionate about the mission and our clients. Our clients and the mission in what we are trying to accomplish must be in the forefront of our minds in everything we do.
02: Integrity, Truth, Reality
We make promises that we can keep, and goals that push us to new heights. Our integrity offers us the opportunity to learn and improve by being honest about what works and what doesn’t. By being true to the data and producing realistic metrics, we are able to create plans and resources to achieve our goals.
03: Kindness, Empathy, Grace
We will empathize with everyone's situation, provide positive and constructive feedback with kindness, and accept opportunities for improvement with grace and gratitude. We use this principle across the organization to collaborate and build lines of open communication.
04: Resilience, Mettle, Perseverance
We will persevere – even in difficult and challenging situations. Our ability to recover from missteps and failures in a positive way will help us to be successful in our mission.
05: Humility, Gratitude, Learning
We will be true learners by showing humility and gratitude in our work. We recognize that the smartest person in the room is the one who is always listening, learning, and willing to shift their thinking.
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