Senior Data Scientist - Python/R, SQL, EDA (Onsite, Islamabad, PKR Salary)

Requirements:

• 7 Years of relevant experience in data analytics, machine learning, or a related field.
• Proficiency in Python (preferred) or R programming languages.
• Strong SQL skills for data querying and transforming large datasets
• Expertise in exploratory data analysis (EDA), feature engineering, and statistical modelling.
• Hands-on experience with advanced machine learning algorithms and optimization techniques
• Understanding of MLOps practices for model deployment and lifecycle management (preferred)
• Strong analytical, problem-solving, and communication skills with the ability to work cross-functionally.
• Proficiency in data visualization tools (Tableau, Power BI) and storytelling with data.
• Excellent communication and collaboration abilities.

Responsibilities:

• Perform advanced exploratory data analysis (EDA) to uncover patterns, correlations, and actionable insights using large and complex datasets.
• Design and implement robust feature engineering and selection techniques to optimize model accuracy and efficiency.
• Formulate hypotheses and validate them through rigorous statistical testing and experimentation frameworks.
• Develop, train, and optimise machine learning models (including supervised, unsupervised, and ensemble methods) for predictive and prescriptive analytics.
• Write optimised SQL queries and leverage cloud-based data platforms (e.g., AWS Redshift, Snowflake, BigQuery) for data extraction and transformation.
• Utilise Python (preferred) and R for data wrangling, modelling, and automation, incorporating libraries such as Pandas, NumPy, Scikit-learn, and PyTorch/TensorFlow where applicable.
• Create dynamic dashboards and interactive visualizations using modern BI tools (e.g., Power BI, Tableau) and consider integration with cloud services for scalability.
• Document workflows, modelling approaches, and analytical findings clearly, ensuring reproducibility and compliance with organizational standards.
• Communicate insights effectively to technical and non-technical stakeholders, using storytelling and visualization techniques to drive business decisions.
• Collaborate cross-functionally with data engineers, ML engineers, and business teams to design end-to-end data solutions and deploy models into production environments.
• Stay updated with emerging trends in AI/ML, big data technologies, and MLOps practices to continuously improve analytical capabilities.