Machine Learning Engineer & Data Scientist

Long View Systems; Vancouver; Hybrid; CAD 90,000 - 120,000; Full time

Job summary

A tech solutions provider is seeking a Machine Learning Engineer/Data Scientist in Vancouver. This role involves owning the end-to-end analytics lifecycle, collaborating with various stakeholders, and implementing innovative ML solutions in Azure. The ideal candidate will have 5-6 years of applied data science experience, strong skills in Python, and a proven track record of shipping impactful models. The company values integrity and promotes personal development through regular sessions with team leads.

Qualifications

• 5-6 years of applied data science with shipped models impacting KPIs.
• Experience with Python and statistical modeling.
• Strong MLOps collaboration and customer engagement skills.

Responsibilities

• Own end-to-end analytics lifecycle from problem framing to monitoring.
• Lead discovery to translate business goals into analytical problems.
• Design and implement ML platforms and pipelines in Azure.

Skills

Python

Statistical modeling

ML techniques

Interpersonal skills

Presentation skills

Consulting

Problem-solving

Tools

Azure ML

Databricks

SQL

MLflow

Job description

Are you passionate about driving high value solutions to your clients and want to work with a team in a company that believes in Integrity, Competence, Value and Fun?

We are looking for Machine Learning Engineer/Data Scientist for our Data & Dynamics team to be based out of our Calgary, Edmonton, Toronto and Vancouver branch. You will own the end‑to‑end analytics lifecycle—from problem framing and hypothesis design to model development, validation, deployment, and monitoring—collaborating closely with engineers, architects, and business stakeholders.

A Day in Life

• Detailed documentation including conceptual design, logical design, physical design, bill of materials, as-built diagrams, knowledge transfer materials, FAQs, transition to operations information
• Being one of the few trusted advisors for clients, building long‑term relationships that will further business value
• Attending and representing Long View at the latest industry events
• Lead discovery to translate business goals into well‑scoped analytical problems, measurable KPIs, and model success criteria
• Build reproducible experiments and models (classification, regression, forecasting, NLP/LLMs) using Python and Azure ML/Databricks, document assumptions and limitations
• Engineer and select features; perform rigorous validation (cross‑validation, leakage checks), bias/variance trade‑off, and error analysis; apply Responsible AI practices
• Partner with ML Engineers for operational models with CI/CD, experiment tracking (ML flow), model registries, and online/offline evaluation pipelines
• Design and evaluate GenAI use cases when relevant (prompt engineering, evaluation harnesses, RAG with Azure AI Search, grounded generation, safety testing)
• Communicate results and trade‑offs to non‑technical stakeholders; create compelling visuals and narratives; facilitate decisions that balance accuracy, cost, and operational risk
• Architect and implement ML platforms and pipelines in Azure (Azure Machine Learning, Azure Databricks, Azure Synapse/Microsoft Fabric, Azure Data Lake Storage, Event/Service Bus)
• Participate in discovery workshops, solution estimation, Statements of Work inputs, and stakeholder demos; produce clear design docs, runbooks, and handover materials
• Track personal time billings and report them in a timely manner
• Attend a quarterly Career Life Planning session with your Team Lead or Manager to discuss your interests, training opportunities, your utilization, and other exciting topics
• Contribute to various government audits and special programs that Long View participates in every year. Part of your duties will be to participate in these programs where needed as they relate to your technology area(s)
• Attending and representing Long View at the latest industry events

What You Bring

• A minimum of 5-6 years in applied data science/analytics with shipped models impacting business KPIs
• Experience with Python, statistical modeling, experiment design, and ML techniques (tree‑based methods, GLMs, time‑series, causal inference basics)
• Experience with Azure ML, Databricks/Spark, SQL, and data wrangling at scale; familiarity with Fabric/Synapse data pipelines
• Strong MLOps collaboration (MLflow, model lifecycle, monitoring/alerts, data quality checks such as Great Expectations equivalent patterns)
• Exceptional customer engagement, interpersonal, stakeholder facilitation, presentation and overall communication skills
• Consulting experience
• Good understanding of ITIL Incident Management
• Excellent problem‑solving and multitasking skills

What Makes You Extra Awesome

• Certifications: DP‑100, DP‑203, AI‑102, AZ‑900/AI‑900
• Experience with NLP/LLMs and RAG on Azure
• Probabilistic modeling; optimization, Bayesian methods, A/B testing at scale
• Experience in supply chain analytics or inventory optimization