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As a Senior Data Scientist, you will be responsible for analyzing complex data sets to identify trends, develop insights, and support decision-making processes. You will utilize your expertise in Python or R, SQL, exploratory data analysis (EDA), feature engineering, and basic machine learning models to deliver actionable insights. You will work closely with cross-functional teams to drive insights, build models and create data visualizations using tools like Tableau or Power BI
Job Responsibility:
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
Requirements:
07 Years of relevant experience in data analytics, machine learning, or 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