Senior Data Scientist PythonR, SQL, EDA

HR POD - Hiring Talent Globally

Job Location:

Islamabad - Pakistan

Monthly Salary: Not Disclosed

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

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.

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 ...

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.