Data Scientist

The Ksquare Group seeks a Data Scientist to design, develop, validate, and deliver data-driven solutions to complex business challenges. This role requires strong Python development skills, advanced statistical knowledge, and hands-on experience building and evaluating machine learning models.

The ideal candidate combines rigorous analytical thinking with software engineering best practices, ensuring that models and analytical solutions are reliable, maintainable, well-documented, and suitable for production environments. They should also demonstrate sound judgment when evaluating emerging AI technologies and determining when machine learning, generative AI, or agent-based systems are appropriate for a given problem.

Key Responsibilities:

• Design, develop, and implement machine learning models to solve business problems using Python as the primary development language.
• Perform advanced data exploration, preparation, transformation, and feature engineering using large and complex datasets.
• Apply statistical methods, experimental design, hypothesis testing, and causal inference techniques to support data-driven decision-making.
• Develop and evaluate predictive models using appropriate algorithms and libraries, including Scikit-learn, XGBoost, LightGBM, and Statsmodels.
• Apply deep learning techniques using TensorFlow or PyTorch when justified by the problem and business requirements.
• Validate model performance beyond traditional accuracy metrics, incorporating business impact, bias assessment, and performance across different subgroups.
• Work with large-scale data using advanced SQL and PySpark for data exploration and transformation.
• Access and manage data stored in cloud environments, including Google Cloud Storage (GCS) and Azure Blob Storage.
• Develop clean, modular, reusable, and maintainable Python code following established software development standards.
• Use Git for version control and collaborate with cross-functional teams through structured development workflows.
• Track experiments, model versions, and performance using MLflow or equivalent experiment management tools.
• Document models and analytical solutions, including their objectives, data requirements, assumptions, limitations, and validation results.
• Follow best practices for project organization, data versioning, credential management, and secure handling of data.
• Evaluate the appropriate use of generative AI tools, large language models (LLMs), and agent-based systems, ensuring that AI-generated outputs are critically reviewed and validated.

Required Qualifications:

• Strong, advanced proficiency in Python as the primary development language.
• Solid experience in data science, statistical modeling, and machine learning.
• Hands-on experience with the following libraries and tools: Pandas and/or Polars, Scikit-learn, XGBoost and/or LightGBM, Statsmodels and SciPy
• Advanced SQL skills and experience working with large datasets.
• Experience with PySpark for data exploration and data transformation.
• Working knowledge of cloud-based data storage, particularly GCS and/or Azure Blob Storage.
• Strong understanding of statistical inference, experimental design, hypothesis testing, and causal inference.
• Experience validating models using appropriate statistical and business-oriented metrics.
• Proficiency with Git and software development best practices.
• Experience with MLflow or equivalent tools for experiment tracking and reproducibility.
• Ability to produce production-quality code beyond exploratory notebooks.
• Strong analytical thinking, problem-solving skills, and the ability to communicate technical findings and model limitations.

Preferred Qualifications:

• Experience developing deep learning models using TensorFlow or PyTorch.
• Familiarity with data and model versioning practices.
• Experience assessing model bias, fairness, and performance across different population segments.
• Familiarity with responsible AI practices and the practical application of generative AI, LLMs, and agent-based systems.
• Experience collaborating within established engineering standards and multidisciplinary teams.