Data Scientist
Invisible Technologies
AWSCloudGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSpark
Lead Data Scientist, Machine Learning Engineer 2025
Aimpoint Digital
full-time
Posted on:
Location: Massachusetts • 🇺🇸 United States
Visit company websiteJob Level
Senior
Tech Stack
AWSAzureGoogle Cloud PlatformKubernetesPythonPyTorchScikit-LearnSparkSQLTensorflowTerraform
About the role
- Become a trusted advisor working with clients to design end-to-end analytical solutions
- Work independently to solve complex data science use-cases across various industries
- Design and develop feature engineering pipelines, build ML & AI infrastructure, deploy models, and orchestrate advanced analytical insights
- Write code in SQL, Python, and Spark following software engineering best practices
- Collaborate with stakeholders and customers to ensure successful project delivery
- Lead and contribute to our practice development and business development
Requirements
- Degree in Computer Science, Engineering, Mathematics, or equivalent experience
- Experience with building high quality Data Science models to solve a client's business problems
- Experience with managing stakeholders and collaborating with customers
- Strong written and verbal communication skills required
- Ability to manage an individual workstream independently
- 3+ years of experience developing and deploying ML models in any platform (Azure, AWS, GCP, Databricks etc.)
- Ability to apply data science methodologies and principles to real life projects
- Expertise in software engineering concepts and best practices
- Self-starter with excellent communication skills, able to work independently, and lead projects, initiatives, and/or people
- Willingness to travel.
- Want to stand out? Consulting Experience
- Databricks Machine Learning Associate or Machine Learning Professional Certification.
- Familiarity with traditional machine learning tools such as Python, SKLearn, XGBoost, SparkML, etc.
- Experience with deep learning frameworks like TensorFlow or PyTorch.
- Knowledge of ML model deployment options (e.g., Azure Functions, FastAPI, Kubernetes) for real-time and batch processing.
- Experience with CI/CD pipelines (e.g., DevOps pipelines, GitHub Actions).
- Knowledge of infrastructure as code (e.g., Terraform, ARM Template, Databricks Asset Bundles).
- Understanding of advanced machine learning techniques, including graph-based processing, computer vision, natural language processing, and simulation modeling.
- Experience with generative AI and LLMs, such as LLamaIndex and LangChain
- Understanding of MLOps or LLMOps.
- Familiarity with Agile methodologies, preferably Scrum
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