Lead Decision Scientist - Machine Learning Engineer

2 days ago

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Aimpoint Digital

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

51 - 200

Description

• Aimpoint Digital is a premier analytics consulting firm driving business value through data strategy, analytics, decision sciences, and engineering. • Focus on enabling clients to get the most out of their data. • Work with all levels of the client organization to build value-driving solutions that extract insights. • Typical solutions will utilize machine learning, AI, statistical analysis, automation, optimization, and data visualization. • Expected to work independently on client engagements, aid in business development, and contribute innovative ideas to the company. • Serve as a trusted advisor designing end-to-end analytical solutions and solving complex data science use-cases. • Write code in SQL, Python, and Spark following software engineering best practices. • Collaborate with stakeholders to ensure successful project delivery.

Requirements

• Databricks experience is required. • Degree in Computer Science, Engineering, Mathematics, or equivalent experience. • Experience with building high quality Data Science models using Databricks ML to solve client's business problems. • Experience in deploying models via model serving within Databricks. • 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 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. • 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.