Position Title: Data Scientist
Location: Toronto, ON - 3 days - onsite Job Overview

We are seeking a skilled and analytical Data Scientist to join our team and drive business value through the application of advanced analytics and machine learning techniques. As a Data Scientist, you will work with large and complex datasets to extract insights, build predictive models, and help solve business challenges across various functions such as marketing, operations, finance, and customer experience.

You will collaborate with cross-functional teams, including data engineers, business analysts, and product managers, to identify key business problems and design data-driven solutions. The ideal candidate has a strong technical background in data science and machine learning, as well as the ability to communicate complex analyses in a clear and actionable way.

Key Responsibilities

• Data Exploration & Analysis: Perform exploratory data analysis (EDA) to identify trends, patterns, and insights within large datasets. Understand the business context to develop hypotheses and identify key data points for analysis.
• Model Development: Design, build, and implement machine learning models and algorithms (e.g., regression, classification, clustering, NLP, time-series forecasting) to solve business problems and drive decision-making.
• Data Preprocessing & Feature Engineering: Clean, preprocess, and transform raw data into usable formats for analysis. Apply techniques like feature selection and engineering to optimize model performance.
• Predictive Analytics & Forecasting: Develop and deploy predictive models that forecast key business metrics, customer behavior, and market trends, helping the organization make proactive decisions.
• Collaboration with Stakeholders: Work closely with business leaders and stakeholders to understand their needs and translate them into technical requirements. Provide actionable insights that inform business strategy.
• Data Visualization & Reporting: Communicate findings effectively through data visualizations, dashboards, and reports, making complex insights accessible to non-technical stakeholders.
• Model Evaluation & Tuning: Evaluate the performance of machine learning models using appropriate metrics (e.g., accuracy, precision, recall, AUC, RMSE) and fine-tune them for improved results.
• A/B Testing & Experimentation: Design and conduct A/B tests, experiments, and data-driven studies to test hypotheses and validate the effectiveness of new strategies or products.
• Automation & Optimization: Build scalable and automated pipelines for model training, evaluation, and deployment, ensuring that models can be regularly updated as new data becomes available.
• Research & Innovation: Stay up to date with the latest trends, tools, and techniques in data science, machine learning, and AI. Continuously experiment with new methodologies and approaches to solve business challenges.

Requirements

Education & Experience:

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• 3+ years of experience in a data science role, with a proven track record of applying machine learning and statistical methods to solve real-world problems.
• Experience working with large, complex datasets and databases.
• Strong understanding of machine learning techniques, statistical modeling, and data analysis.

Technical Skills:

• Proficiency in programming languages such as Python, R, or Scala for data analysis and machine learning.
• Strong experience with machine learning libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM).
• Hands-on experience with data manipulation and analysis using tools such as Pandas, NumPy, and SQL.
• Expertise in data visualization tools like Matplotlib, Seaborn, Plotly, or BI tools (e.g., Tableau, Power BI) to present results.
• Experience with cloud platforms and tools (e.g., AWS, Google Cloud, Azure) for deploying models and managing data pipelines.
• Familiarity with big data technologies like Hadoop, Spark, or distributed computing frameworks is a plus.
• Knowledge of data warehousing, ETL processes, and data storage solutions (e.g., SQL, NoSQL, and cloud data platforms).

Soft Skills:

• Strong problem-solving skills with the ability to think critically and apply analytical techniques to business challenges.
• Excellent communication skills, with the ability to explain complex concepts to non-technical stakeholders in a clear, concise manner.
• Ability to collaborate effectively across teams, including engineers, product managers, and business leaders.
• Proactive, self-motivated, and able to work independently in a fast-paced, results-oriented environment.
• Detail-oriented, with a focus on producing high-quality, accurate work.

Preferred Qualifications

• Experience in a specific industry (e.g., healthcare, finance, retail, e-commerce) with domain knowledge of relevant business metrics and challenges.
• Familiarity with deep learning techniques, natural language processing (NLP), or computer vision is a plus.
• Experience with model deployment tools and frameworks (e.g., Flask, FastAPI, Docker, Kubernetes, MLflow) for creating production-ready solutions.
• Experience with advanced statistical techniques, such as Bayesian methods, time-series analysis, or survival analysis.
• Familiarity with DevOps practices for version control (Git) and CI/CD pipelines for model deployment.

Benefits

• Competitive salary and performance-based bonuses.
• Comprehensive health, dental, and vision insurance.
• Flexible working hours and remote work options.
• Retirement plan with company matching.
• Opportunities for professional development, training, and certifications in data science and machine learning.
• Collaborative work environment with a focus on innovation and cutting-edge technologies.