Job Title: DataIku Data Scientist – Machine Learning & Advanced Analytics

Location: Scarborough, ON (hybrid)

Duration: 12-14 months contract with possible extension

Summary

We are seeking a Data Scientist to join our team to lead the design, development, and operationalization of advanced machine learning solutions. The successful candidate will leverage large-scale structured and unstructured data to deliver predictive insights, optimize business processes, and enable data-driven decision making across the enterprise.

Key Responsibilities

- Design, develop, deploy, and maintain machine learning, deep learning, and AI-driven solutions that address complex business and operational challenges.
- Build scalable predictive and prescriptive analytics models using large-scale datasets to improve business performance, customer experience, operational efficiency, and risk management.
- Translates business needs to technical specifications and evaluates existing data visualization systems to improve them
- Perform advanced data exploration, feature engineering, model development, validation, and performance monitoring across the model lifecycle.
- Develop and operationalize end-to-end ML pipelines, including data ingestion, model training, scoring, scheduling, monitoring, and retraining.
- Leverage big data technologies and distributed computing frameworks to process and analyze high-volume datasets efficiently.
- Collaborate with business stakeholders, product teams, and technology partners to identify opportunities where AI/ML can create measurable business value.
- Conduct statistical analysis, experimentation, and model evaluation to identify trends, anomalies, and actionable insights.
- Research and evaluate emerging technologies, algorithms,



and data science methodologies to drive innovation and continuous improvement.
- Develop recommendation engines, classification models, forecasting solutions, and anomaly detection frameworks to support strategic business initiatives.
- Translate complex analytical findings into clear insights and recommendations for senior leadership and executive stakeholders.
- Partner with various line of business teams, Data Engineering, DevOps, and platform teams to ensure scalable, production-ready analytics solutions are deployed and maintained.

Required Qualifications & Experience

- 7+ years of experience in Data Science, Machine Learning, Artificial Intelligence, Advanced Analytics, or a related discipline.
- Strong proficiency in Python (preferred) or SAS for statistical modeling, machine learning, and data analysis.
- Expert-level SQL skills with hands-on experience working with large-scale enterprise datasets.
- Strong experience with Big Data technologies, including:
- Apache Spark
- Hadoop ecosystem
- Distributed data processing frameworks

- Hands-on experience developing, deploying, monitoring, and maintaining machine learning models within Dataiku DSS.
- Solid experience working with ITSM data, including ServiceNow incident, change, problem, CMDB, Dynatrace and other operational datasets.
- Power BI and advanced data visualization
- ServiceNow platform analytics and reporting




- Experience building and operationalizing:

- Classification models
- Recommendation systems
- Predictive analytics solutions
- Anomaly detection models
- Time-series forecasting models

- Experience implementing automated scoring and model deployment pipelines using scheduling and orchestration frameworks.
- Hands-on experience with cloud-based ML platforms such as:

- AWS SageMaker
- Azure Machine Learning
- Databricks

- Strong experience with enterprise data platforms including:

- Netezza
- Amazon Redshift
- SQL Server/relational databases

- Experience with MLOps practices, model governance, model monitoring, and production deployment frameworks.
- Familiarity with CI/CD, containerized deployments, and cloud-native analytics architectures is considered an asset.

Preferred Qualifications

- Experience building AI/ML solutions for IT Operations (AIOps), Service Management, or Operational Intelligence use cases.
- Experience developing models for:
- Incident prediction
- Change-induced outage prediction
- Root cause analysis
- Event correlation
- Recommendation systems

- Strong understanding of software engineering best practices, version control, and collaborative development environments.
- Excellent communication and stakeholder management skills with the ability to present complex technical concepts to non-technical audiences and senior leadership.
- Banking experience, particularly large enterprise-wide initiatives would be an asset.

Nice-to-Have Skills

- Dataiku MLOps and automation capabilities
- Azure AI / Generative AI solutions
- Feature Store implementation
- LLM and Agentic AI frameworks

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