Lead Data Scientist - Machine Learning Engineer

March 20

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Aimpoint Digital

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Aimpoint Digital is a market-leading analytics, data engineering, operations research, and Artificial Intelligence advisory and solution engineering firm. It empowers organizations to unlock value from data by providing strategic guidance, developing state-of-the-art AI solutions, and implementing cutting-edge analytics applications. Aimpoint Digital partners with various companies to enhance operations, drive data-driven decisions, and maximize ROI through personalized data strategies and optimization solutions.

Machine Learning • Artifical Intelligence • Tableau • Cloud • Data Architecture

51 - 200 employees

🤖 Artificial Intelligence

📋 Description

• Aimpoint Digital is a premier analytics consulting firm focused on delivering solutions via machine learning and statistical modelling. • As a Lead Data Scientist, you will enable clients to maximize their data. • You will work with client organizations to build value-driven solutions.

🎯 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, Git 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.