Data Scientist

Role Overview

• Develop and implement a set of techniques or analytics applications to transform raw data into meaningful information using data-oriented programming languages and visualization software
• Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from large structured and unstructured datasets
• Visualize, interpret, and report data findings
• May create dynamic data reports
• Collaborate with cross-functional teams to align AI projects with business requirements and strategic goals
• Ensure scalability, performance, and optimization of deployed AI models and pipelines.

Requirements

• Bachelor’s or greater degree in Machine Learning, AI, or equivalent professional experience
• Minimum of 1 year of professional experience in AI, application development, machine learning, or a similar role
• Experience in model deployment, MLOps, model monitoring, and managing data/model drift
• Experience with predictive AI (e.g., regression, classification, clustering) and generative AI models (e.g., GPT, Claude LLM, Stable Diffusion)
• Proficiency in programming languages such as Python and SQL
• Proficiency in URLs and API Endpoints, HTTP Requests, Authentication Methods, Response Types, JSON/REST, Parameters and Data Filtering, Error Handling, Debugging, Rate Limits, Tokens, Integration, and Documentation
• Proficiency with cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Databricks, PySpark)
• Familiarity with AI frameworks such as LangChain and machine learning libraries like TensorFlow, PyTorch, and scikit-learn
• Knowledge of deployment tools (e.g., Azure DevOps, Docker, AWS ECS/EKS/Fargate) and CI/CD pipelines (AWS CloudFormation, CodeDeploy)
• Understanding of data engineering principles, including experience with SQL and NoSQL databases (e.g., MySQL, MongoDB, Redis)
• Strong problem-solving and troubleshooting skills
• Familiarity with generative AI techniques, such as retrieval-augmented generation (RAG) patterns
• Experience with Graph database technology a plus. (e.g. Neo4J, Ontotext)
• Ability to collaborate effectively across teams
• Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

Tech Stack

• AWS
• Azure
• Cloud
• Docker
• MongoDB
• MySQL
• Neo4j
• NoSQL
• PySpark
• Python
• PyTorch
• Redis
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade
• Company paid holidays
• Personal Days
• Sick Leave
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs
• Other benefits as provided by local policy and eligibility