Senior Data Scientist

Recruitment Intelligence; Islamabad; On-site; PKR 2,400,000 - 4,800,000; Full time

Benefits offered by this job

Competitive salary

Professional growth

Collaborative team

Job summary

Recruitment Intelligence in Islamabad seeks an experienced Data Scientist to develop scalable, time-series forecasting and predictive analytics solutions. You will implement models using Python, PySpark, Databricks, and Azure DevOps, collaborating with data engineers and business stakeholders.

The role emphasizes data manipulation with pandas/NumPy, model validation, and delivering actionable insights. You will work on large datasets, ensure reproducibility, and contribute to production-ready

Qualifications

• Bachelor's or master's in a relevant field (DS/CS/Statistics/Math/Engineering).
• 5+ years of professional Python experience.
• Strong pandas proficiency for data cleansing, reshaping, merging and large-scale ops.
• Solid NumPy knowledge for numerical computing and data processing.

Responsibilities

• Develop, evaluate, and deploy time-series forecasting models for business use.
• Build predictive analytics solutions to support data-driven decisions.
• Prepare, cleanse, transform, and feature engineer data with Python/pandas.
• Work with PySpark on large datasets and optimize data workflows.
• Develop and maintain data pipelines and analytics in Databricks.
• Analyze trends, seasonality, outliers, and patterns in historical data.
• Select forecasting techniques based on business needs and validate performance.
• Collaborate with stakeholders, data engineers, and tech teams to translate problems.
• Use Azure DevOps for source control, work items, CI/CD, and collaboration.
• Document methodologies, assumptions, code, and model results clearly.
• Monitor model performance and propose improvements when needed.
• Follow code quality, testing, and deployment standards.

Skills

Python

pandas

NumPy

Matplotlib

PySpark

Databricks

Git

Azure DevOps

SQL

Linux

Education

Bachelor's or Master's in Data Science/CS/Statistics/Math/Engineering

Tools

Databricks

Git

Azure DevOps

SQL

Job description

We are looking for a Data Scientist with 5+ years of overall professional experience to develop scalable, data-driven solutions focused on time-series forecasting, predictive analytics, and advanced data manipulation. The ideal candidate will have strong hands-on experience with Python, pandas, NumPy, Matplotlib, PySpark, Databricks, Git, and Azure DevOps, along with a solid understanding of statistical modeling and data analysis principles.

Key Responsibilities

• Develop, evaluate, and deploy time-series forecasting models for business and commercial applications.
• Build predictive analytics solutions to support data-driven decision-making.
• Perform data preparation, cleansing, transformation, and feature engineering using Python and pandas.
• Work with large datasets using PySpark and optimize data processing workflows.
• Develop and maintain data pipelines and analytical workflows in Databricks.
• Analyze trends, seasonality, outliers, and other patterns in historical data.
• Select appropriate forecasting and predictive modeling techniques based on business requirements.
• Validate model performance using relevant statistical and business metrics.
• Collaborate with business stakeholders, data engineers, and technology teams to translate business problems into analytical solutions.
• Use Azure DevOps for source control, work-item tracking, CI/CD, and collaborative development.
• Document analytical methodologies, assumptions, code, and model results clearly.
• Monitor model performance and recommend improvements when data or business conditions change.
• Follow established standards for code quality, testing, version control, and deployment.

Required Qualifications and Skills

• Bachelor's or master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Strong programming skills in Python, with 5+ years of relevant professional experience.
• Advanced proficiency in pandas, including data cleansing, reshaping, merging, aggregation, group-based operations, and efficient manipulation of large datasets.
• Strong working knowledge of NumPy for numerical computing, array operations, and efficient data processing.
• Experience using Matplotlib to create clear and insightful visualizations for exploratory analysis and model evaluation.
• Practical experience with time-series forecasting techniques, including trend and seasonality analysis, lag features, rolling statistics, and forecast evaluation.
• Strong understanding of predictive analytics, statistical analysis, and model validation.
• Hands‑on experience with PySpark for distributed data processing.
• Experience working with Databricks notebooks, jobs, clusters, and data workflows.
• Familiarity with SQL for data extraction, joining, aggregation, and analysis.
• Experience using Git for source control, branching, merging, pull requests, and collaborative development.
• Experience using Azure DevOps for version control, task management, CI/CD processes, and release coordination.
• Working knowledge of Linux operating systems and familiarity with the command line.
• Understanding of data quality, reproducibility, and documentation best practices.
• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience applying forecasting and predictive analytics to sales, demand, pricing, revenue, or other commercial datasets.
• Familiarity with Azure data and analytics services.
• Experience with data visualization tools such as Power BI or similar platforms.
• Knowledge of model monitoring, performance tracking, and production support practices.
• Experience optimizing Python, pandas, SQL, or PySpark code for performance and scalability.
• Familiarity with Agile delivery practices and collaborative software development.

What We Offer

• The opportunity to work on practical forecasting and predictive analytics solutions with measurable business impact.
• Collaboration with experienced data, technology, and business teams.
• Exposure to modern data platforms and scalable analytics workflows.
• A professional environment that values continuous learning, innovation, and high-quality delivery.