About the Role

The Lead Data Scientist will lead the development of Smart Hawk's data science and advanced

analytics capabilities, applying statistical methods, machine learning, and artificial intelligence to

solve business and operational challenges.The successful candidate will work with historical customer, service, operational, and financial

data to identify trends, develop analytical models, and improve business decision-making.

The position will also contribute to establishing an integrated analytical data foundation that

supports customer lifecycle visibility, service-to-invoice reconciliation, operational reporting, and

future AI-driven capabilities.

This is a hands-on technical leadership position requiring strong analytical expertise,

independent problem-solving skills, and the ability to work collaboratively with operational,

financial, and technical stakeholders.

Key Responsibilities

Data Science and Predictive Analytics

- Design, develop, evaluate, and implement statistical and machine learning models to

analyze customer behaviour, service patterns, operational performance, and business

trends.

- Apply techniques such as regression, classification, clustering, time-series analysis, and

anomaly detection to address relevant business problems.

- Analyze historical customer and service data to identify trends, understand changes in

customer activity, and investigate potential customer retention risks.

- Conduct exploratory data analysis, feature engineering, experimentation, and model

validation to improve the reliability and effectiveness of analytical solutions.

Data Engineering and Analytical Infrastructure

- Assess existing operational, customer, invoicing, payroll, and reporting data sources to

identify opportunities for integration and advanced analysis.

- Design analytical data models and develop data extraction, transformation, cleansing,

and validation processes using Python, SQL, and appropriate data technologies.

- Establish data-quality controls and support the development of scalable analytical

infrastructure for reporting, statistical analysis, and machine learning.

- Collaborate with application owners, technology providers, and relevant stakeholders to

define data integration requirements and improve information flow between business

systems.Artificial Intelligence and Advanced Analytics

- Research, prototype,



and evaluate artificial intelligence and natural language processing

solutions for analyzing security reports and operational information.

- Explore techniques for extracting meaningful information from structured and

unstructured data, identifying recurring patterns, and generating actionable analytical

insights.

- Evaluate appropriate machine learning and generative AI approaches based on business

requirements, data availability, model performance, and implementation feasibility.

- Apply model evaluation, monitoring, documentation, and responsible AI practices to

support reliable and secure analytical solutions.

Operational and Business Intelligence

- Analyze customer, service, and invoicing records to help identify discrepancies,

incomplete records, and opportunities to improve business processes.

- Develop analytical dashboards, visualizations, and performance indicators that provide

relevant insights to operations, accounting, and executive management.

- Support data-driven improvements to the customer lifecycle, including customer

onboarding, service assignment, service completion, invoicing, and follow-up processes.

- Translate analytical findings into practical recommendations that help improve

operational visibility and business decision-making.

Technical Leadership and Collaboration

- Lead data science initiatives from business problem definition and technical design

through experimentation, model evaluation, and implementation.

- Collaborate with operations, finance, accounting, and technical stakeholders to identify

analytical priorities and translate business requirements into data-driven solutions.

- Establish best practices for statistical analysis, machine learning development, code

quality, documentation, and analytical governance.

- Communicate technical findings, model results, and recommendations to both technical

and non-technical stakeholders, including senior management.

- Provide technical guidance and knowledge sharing to colleagues and future analytics or





data science team members as the function develops

Required Qualifications

- Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics,

Engineering, or a related quantitative discipline.

- At least five years of relevant professional experience in data science, machine learning,

advanced analytics, or a closely related field, including experience independently

delivering analytical solutions.

- Robust programming skills in Python and SQL, with hands-on experience in data analysis,

data preparation, feature engineering, and statistical modelling.

- Experience developing and evaluating machine learning models using methods such as

regression, classification, clustering, forecasting, or anomaly detection.

- Experience working with structured and unstructured datasets and developing analytical

data workflows.

- Understanding of statistical methods, model validation, performance evaluation, and

machine learning best practices.

- Experience using common data science libraries and frameworks, such as Pandas,

NumPy, Scikit-learn, or equivalent technologies.

- Strong analytical, problem-solving, communication, and stakeholder collaboration skills.

- Ability to work independently, manage technical priorities, and translate business

requirements into practical analytical solutions.

Preferred Qualifications

- Master's degree or higher in Data Science, Computer Science, Statistics, Mathematics,

Artificial Intelligence, or a related quantitative field.

- Experience with cloud-based data platforms, distributed data processing, or analytical

environments such as Azure, AWS, Google Cloud, Databricks, or equivalent technologies.

- Experience developing or evaluating natural language processing, large language model,

or generative AI applications.

- Experience with predictive analytics relating to customers, operations, service demand,

or business performance.

- Familiarity with data visualization and business intelligence tools such as Power BI,

Tableau, or similar platforms.

- Experience deploying machine learning models, monitoring model performance, or

working with MLOps practices

Pay: $135,000.00-$142,000.00 per year

Benefits:

- Dental care
- Disability insurance
- Life insurance
- On-site parking
- RRSP match

Work Location: Hybrid remote in Canada

📌 Lead Data Scientist (Canada)
🏢 Smarthawkcanada
📍 Canada