Data Scientist

KTek Resourcing; Calgary; On-site; CAD 80,000 - 110,000; Full time

Job summary

A data science consulting company in Calgary is seeking a skilled Data Scientist to leverage statistical and machine learning techniques for strategic insights. Ideal candidates should have a strong background in mathematics and experience with data analysis tools like Python and SQL. Responsibilities include developing predictive models, collaborating with teams, and communicating findings effectively. Offers long-term career growth opportunities.

Qualifications

• Bachelor’s or Master’s in Mathematics, Computer Science, Statistics, Engineering, or related field. PhD is a plus.
• Strong mathematical and statistical background (linear algebra, calculus, probability, etc.)
• Excellent logical reasoning and analytical problem-solving skills.
• Strong hands-on experience in Python.
• Experience with SQL and data visualization tools.
• Experience with machine learning algorithms and statistical modeling techniques.

Responsibilities

• Develop, test, and deploy statistical models and predictive algorithms.
• Apply mathematical concepts to solve real-world problems.
• Perform data wrangling and exploratory data analysis.
• Collaborate with cross-functional teams to deliver actionable insights.
• Communicate findings clearly through data visualizations and reports.
• Implement and maintain automated data pipelines.

Skills

Mathematical modeling

Statistical analysis

Logical reasoning

Problem-solving

Python

SQL

Data visualization tools

Machine learning algorithms

Education

Bachelor’s or Master’s in Mathematics, Computer Science, Statistics, Engineering

Tools

Tableau

Power BI

matplotlib

seaborn

Spark

Hadoop

Job description

Job Title: Data Scientist

Location: Calgary, Alberta

Duration: Long Term

Job Opening:

Who are we looking for?

We are seeking a highly analytical and intellectually curious Data Scientist with a strong foundation in mathematical modeling, logical reasoning, and problem-solving. The ideal candidate will leverage advanced statistical and machine learning techniques to uncover insights, build predictive models, and guide strategic business decisions.

Key Responsibilities:

· Develop, test, and deploy statistical models, predictive algorithms, and optimization frameworks.

· Apply mathematical concepts such as linear algebra, calculus, probability theory, and discrete mathematics to solve real-world problems.

· Perform data wrangling, exploratory data analysis (EDA), and feature engineering on structured and unstructured datasets.

· Collaborate with cross-functional teams to identify problems, design experiments, and deliver actionable insights.

· Use logical frameworks to structure complex problems, define hypotheses, and validate outcomes.

· Communicate findings clearly through data visualizations, technical reports, and stakeholder presentations.

· Optimize and validate models using statistical tests, A/B testing, and performance metrics like ROC-AUC, RMSE, etc.

· Implement and maintain automated data pipelines and analytical dashboards.

Required Skills & Qualification:

· Bachelor’s or Master’s in Mathematics, Computer Science, Statistics, Engineering, or related field. PhD is a plus.

· Strong mathematical and statistical background (linear algebra, calculus, probability, etc.)

· Excellent logical reasoning and analytical problem-solving skills.

· Strong hands-on experience in Python.

· Experience with SQL, data visualization tools (Tableau, Power BI, matplotlib, seaborn).

· Experience with machine learning algorithms, optimization methods, and statistical modeling techniques.

· Knowledge of big data tools (e.g., Spark, Hadoop) is a plus.

· Strong communication skills and ability to explain complex concepts to non-technical stakeholders.

Preferred Qualifications:

· Experience working with real-world datasets in domains like finance, healthcare, logistics, or retail.

· Publications in mathematical modeling, optimization, or AI/ML research (optional).

· Exposure to cloud platforms (AWS, GCP, Azure) and MLOps practices.