Job Description

HM Note: This hybrid contract role is five (5) days in office. Candidate resumes must include first and last name, email and telephone contact information.

Description

Responsibilities:

Lead the development and delivery of functional and ministry-specific analytics to support evidence-based decision-making and produce actionable insights. Work closely with client's groups to assess current data analytics and reporting capabilities, gather future-state requirements and identify further opportunities for improvement. Facilitate decision-making and manage client expectations. Own the execution of analytics initiatives including end-to-end reporting and data set delivery. Develop robust statistical models and machine learning algorithms to model business scenarios and extract valid inferences. Participate in documentation, development, testing, and end user training. Work with functional area experts, Data Architects and ETL Developers and stakeholders to understand complex business issues and develop appropriate Business Intelligence solutions. Design methods to capture, structure, transform, and process data to be used to generate models. Build data models that provide information which is accurate, easy to understand and unbiased. Communicate complex quantitative analysis in a clear and precise manner, providing useful visuals and summaries. Provide interpretation, advice, and expertise to client groups and other stakeholders, including direction on how to transform analytics into actionable information and proactive insights that support decision making.

General Skills:

Excellent analytical, problem-solving and decision-making skills, verbal and written communication skills, interpersonal skills and team work skills. Outstanding consulting and relationship management skills, with proven ability to elicit requirements, develop/consult on options and solutions, and provide effective guidance Adept at communicating to both technical and non-technical audiences Experience with a range of analytical methods, techniques and tools such as, but not limited to: statistical analysis and modelling, data mining, machine learning and algorithms, natural language processing, and other related disciplines at the specified experience level Ability to manipulate and analyze complex, high-volume data from structured and unstructured sources Experience developing data extraction, transformation, and load functionality for large relational and multi-dimensional data stores Experience designing high quality interfaces to present information in a meaningful way to end users Broad understanding of data management, financial and business analysis, database architecture, and information visualization Proficiency in query languages and experience constructing complex query statements Experience in one or more programming or scripting languages Strong investigative and logic skills. Proficiency in mathematics and statistics Awareness of emerging Business Intelligence trends and directions Functional area experience as required Experience with analytical software such as R, PowerPivot, Matlab, SPSS, or SAS an asset Proficiency with desktop analysis software including Microsoft Excel, Access, VBA


Skills
Experience and Skill Set Requirements

Role Overview:

We are looking for a data scientist to support various initiatives related to the platform as well as the execution of use cases to support the needs of various businesses.

Key Responsibilities

Run and Monitor Inferences

Execute batch inference jobs on Databricks and other environments. Troubleshoot inference issues, log results, and escalate model anomalies.

Pipeline & Evaluation Support

Assist with running DSPy pipelines and loading custom models/configs. Support evaluation runs using internal metrics Track and summarize performance metrics for inference experiments.

Data Handling & Experimentation

Help prepare and clean legal text datasets for inference and evaluation. Annotate outputs to support error analysis and benchmarking. Maintain reproducible scripts/notebooks for inference runs.

Qualifications

Background in

Computer Science, Data Science, or AI/ML

(student or recent grad is good). Familiarity with

Python

and libraries like pandas, scikit-learn, or PyTorch. Exposure to

LLM frameworks

(DSPy, LangChain, Hugging Face, or equivalent). Comfortable working with

Databricks / MLflow

or other cloud ML environments. Detail-oriented with strong organizational skills for tracking experiments.

Must have:

* must have experience Azure data tools - azure, databricks, MLflow, python