1836 Data Scientist III

Job summary

Translate business goals into analytical problems and design, develop, and deploy data science, machine learning, and generative AI solutions, including agents, chatbots, and retrieval systems. Lead technical planning and collaboration, communicate findings and recommendations to stakeholders, and support continuous improvement and innovation across the analytics community.

Job details

Position title: 1836 Data Scientist III Working Status: Hybrid (3 days on-site with Thursday being the team collaboration day) Client: SunLife Contract Duration: 12 months Location: 1 York Street, Toronto Background Check: Reliability Clearance and Tier 2 checks Role Summary Reporting to the Director, Data Science, as a Senior Data Scientist a successful candidate will focus on supporting Canadian business units in accelerating the growth and application of advanced analytics in driving value. The senior data scientist will leverage practical experience in applying varied data science techniques & offering advice/inputs to help with the design, development and implementation of analytics use cases. Key attributes: Fierce curiosity. A successful candidate is drawn to discovering and leveraging new data and taking on challenging business puzzles. An inquisitive mind. Driven to ask questions to help lead projects to commercial value and not being afraid that the innovation attempts can and will lead to failing. A passion for solving problems. The most successful data scientists’ solution for what the right data to use, they solve using the most suitable (not most advanced) algorithms for the problem at hand and figure out how to execute and deliver with highest efficiency. Technical skills in both data and computer science. The 3 core technical skills would be: in-depth coding knowledge of an analytical tool(s) (i.e., Python); data science techniques and concepts; working with structured data and unstructured data. Thirst for learning. A successful candidate is a data scientist who is constantly updating their knowledge of data science state-of-the-art. Key Responsibilities: Data Science and Machine Learning Translate business goals into analytical problems; Identify optimal algorithms, statistical techniques and/or GenAI architecture suitable for the business problem at hand Work in cross-functional teams to develop ML/data science products, including GenAI applications Apply best-in-breed data science techniques including descriptive, predictive, and machine learning methods from design to implementation Focus on feature engineering, model training, model evaluation, and prompt engineering for LLMs GenAI & Agentic AI Development Design, develop, and deploy GenAI solutions including AI agents, chatbots, and autonomous systems that solve complex business problems Research and pilot the latest GenAI technologies, RAG (Retrieval-Augmented Generation) techniques, and agentic frameworks (LangChain, LangGraph, CrewAI etc.) Implement AI bots/agents with advanced reasoning capabilities and tool use patterns Work with vector databases and embedding models to build intelligent information retrieval systems Technical Leadership & Collaboration Break down broader data science development milestones into actionable goals, activities, and work plans Create and maintain technical design artifacts describing application functionality, data models, interfaces, and integrations Engage and negotiate with stakeholders, make business recommendations with effective presentations of findings at multiple levels of stakeholders Champion continuous improvement and foster innovation within the analytics communi Required Qualifications: Bachelor’s degree in computer science, Statistics, Mathematics, or related field, or equivalent experience 5+ years’ experience in developing and implementing data science techniques Proficient in Python for data science and GenAI application development Experience with writing complex SQL and PySpark queries to extract and integrate data from multiple database sources Proficiency in machine learning including supervised and unsupervised models Demonstrated experience in data transformation, data manipulation, and working with structured vs. unstructured data Strong understanding of APIs, microservices architecture, and cloud-native development Hands-on expertise with GenAI frameworks and LLM APIs with experience developing AI bots/agents with reasoning and tool-use capabilities Strong understanding of RAG techniques, prompt engineering, and fine-tuning methodologies and familiarity with vector databases and embedding models Experience with chatbot development concepts and conversational AI design Exceptional communication skills to articulate complex technical concepts to both technical and non-technical stakeholders Effective and concise oral and written storytelling and insights communication skills Ability to work on multiple projects in parallel while managing constantly changing deadlines and priorities Proven ability to mentor and guide junior data scientists and data engineers Strong problem-solving abilities and analytical skills with keen attention to detail Nice to Have: Experience with AWS services including SageMaker, Lambda, Bedrock, and other AI/ML services Experience with data warehousing, pipelines, and big data technologies such as AWS Glue for ETL, Glue Catalog, Glue Data Quality, and AWS Step Functions.

What you’ll do

Translate business goals into analytical problems and design, develop, and deploy data science, machine learning, and generative AI solutions, including agents, chatbots, and retrieval systems. Lead technical planning and collaboration, communicate findings and recommendations to stakeholders, and support continuous improvement and innovation across the analytics community.

Requirements

Requires a bachelor’s degree in computer science, statistics, mathematics, or a related field (or equivalent experience), plus at least five years of experience developing and implementing data science techniques. Candidates need strong Python, SQL, PySpark, machine learning, and generative AI expertise, along with experience in RAG, LLM APIs, cloud-native systems, stakeholder communication, and mentoring.

Listed skills

• Python · Preferred
• SQL · Preferred
• Machine learning · Preferred
• prompt engineering · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• SQL
• PySpark
• Machine Learning
• Generative AI
• Large Language Models
• Retrieval-Augmented Generation
• Prompt Engineering
• Feature Engineering
• Model Evaluation
• Vector Databases
• Embedding Models
• API Development
• Microservices Architecture
• Cloud-Native Development
• Stakeholder Communication

Job areas

• Data & Analytics
• Technology
• Software
• Science & Research

More jobs from Amyantek