Data Scientist / ML Engineer

Iris Software Inc.; Toronto; Hybrid; CAD 100,000 - 150,000; Full time

Job summary

Iris Software Inc. in Toronto is seeking a Data Scientist / ML Engineer for a full-time hybrid role, focused on data wrangling, SAS-to-PySpark migration, and feature engineering across large datasets for banking clients.

You will build and optimize PySpark pipelines, translate legacy SAS logic, implement robust testing and documentation, and collaborate with business users and data scientists to deliver scalable analytics solutions.

Qualifications

• Strong programming in Python and PySpark for distributed data processing.
• Experience building data pipelines for feature engineering and data prep.
• Ability to read, interpret, and translate legacy SAS code to PySpark.
• Experience managing Python environments with Anaconda/Conda and reproducible workflows.

Responsibilities

• Foundation & feature engineering: setup Python/PySpark dev environments and dependencies.
• Pipeline development: design and deploy data pipelines for ML and analytics.
• Code translation: reverse-engineer SAS programs to extract rules and transformations.
• Platform modernization: convert SAS logic to efficient PySpark code.
• Performance tuning: optimize PySpark for scalability in distributed systems.
• Stakeholder collaboration: align with business users and data scientists to validate outputs.
• Quality assurance: unit testing, data reconciliation, and automated validation.
• Documentation: document designs, lineage, tests, and migration methods.

Skills

Python

PySpark

SQL

Data wrangling

SAS interpretation

Tools

SAS

PySpark

Glue

Sagemaker

Conda/Anaconda

Job description

Iris Software is looking to hire a Data Scientist / ML Engineer for a full-time opportunity in Toronto, ON (hybrid position).

Job Title: Data Scientist / ML Engineer

Location: Toronto, ON (2 days onsite)

Full-time with Iris Software for one of the banks in downtown Toronto

The client is looking for:

• data wrangling
• Tool stack probably in this order - Python, PySpark, Glue, Sagemaker, SAS

Key Responsibilities

• Foundation & Feature Engineering: Lead the foundational setup of Python/PySpark development environments, actively managing complex package dependencies and virtual environments using Conda/Anaconda.
• Pipeline Development: Design, build, and deploy net-new data pipelines focused heavily on feature engineering and data preparation to feed downstream machine learning and analytics use cases.
• Code Translation: Analyze and reverse-engineer existing SAS programs (developed by business users and data scientists) to accurately extract business rules, data transformations, and calculations.
• Platform Modernization: Translate extracted SAS logic into efficient, scalable, and functionally equivalent PySpark code.
• Performance Tuning: Optimize PySpark code for performance, scalability, and maintainability within distributed data processing environments.
• Stakeholder Collaboration: Collaborate closely with business users and data scientists to clarify requirements, validate feature outputs, and resolve discrepancies during the migration process.
• Quality Assurance: Perform robust unit testing, data reconciliation, and automated validation to ensure absolute data parity between legacy SAS outputs and the new PySpark pipelines.
• Documentation: Document technical designs, code lineage, testing results, and migration methodologies for future team scaling.

Required Qualifications

• Strong programming expertise in Python and PySpark for distributed data processing.
• Proven experience building data pipelines specifically for feature engineering, data curation, and advanced data preparation.
• Ability to read, interpret, and reverse-engineer legacy SAS code (such as SAS data steps, procedures, and macros) to extract complex business logic. (Note: Deep SAS development experience is a plus, but the ability to translate it is the core requirement).
• Experience establishing and managing Python environments, ensuring reproducibility, and handling library dependencies using Anaconda/Conda.
• Advanced SQL skills and experience working with large-scale structured datasets.
• Experience with code migration, platform modernization, or translating legacy codebases into modern open-source stacks.
• Strong analytical, problem-solving, and communication skills, with a track record of successfully interfacing directly with business stakeholders.

Preferred Qualifications

• Experience working with cloud-based data platforms (Azure, AWS, or GCP).
• Familiarity with version control tools (Git) and collaborative development workflows.
• Experience with CI/CD pipelines, automated testing, and MLOps principles.
• Exposure to financial services, banking, or other highly regulated industries.
• Knowledge of data governance, data cataloging, and data quality best practices.

Key Skills

• Python & PySpark
• SAS Interpretation & Code Translation
• Distributed Computing & Spark Optimization
• Legacy Platform Modernization
• Agile Delivery

About Iris Software Inc.

With 4,000+ associates and offices in India, U.S.A. and Canada, Iris Software delivers technology services and solutions that help clients complete fast, far-reaching digital transformations and achieve their business goals. A strategic partner to Fortune 500 and other top companies in financial services and many other industries, Iris provides a value-driven approach - a unique blend of highly skilled specialists, software engineering expertise, cutting-edge technology, and flexible engagement models. High customer satisfaction has translated into long-standing relationships and preferred-partner status with many of our clients, who rely on our 30+ years of technical and domain expertise to future-proof their enterprises. Associates of Iris work on mission-critical applications supported by a workplace culture that has won numerous awards in the last few years, including Certified Great Place to Work in India; Top 25 GPW in IT & IT-BPM; Ambition Box Best Place to Work, #3 in IT/ITES; and Top Workplace NJ-USA.