Senior Data Scientist – Forecasting & Production ML

CodeNinja; Lahore; On-site; PKR 2,600,000 - 5,200,000; Full time

Benefits offered by this job

Provident Fund

Gym Membership

Leaves as per the company policy

Company-paid trips

Easy Loan Facility for Employees

Yearly increment

Maternity Benefits

Health Insurance

Job summary

CodeNinja, a full‑stack AI delivery company, seeks a Senior Data Scientist (4+ years) in Lahore, Pakistan, to design, develop, and deploy forecasting and ML solutions for high‑impact business use cases. You will translate complex problems into scalable, production‑ready models that inform strategy and risk decisions.

You'll work on time‑series forecasting, feature engineering, model validation, and deploying pipelines, dashboards, and monitoring with cross‑functional teams across finance, risk,

Qualifications

• 4+ years of hands-on experience in Data Science, Machine Learning, or Predictive Analytics.
• Strong experience with time-series forecasting and predictive modeling.
• Proficiency in Python and libraries such as Pandas, NumPy, Scikit‑learn, and XGBoost.
• Experience with forecasting techniques such as ARIMA, SARIMA, Prophet, or similar methods.
• Strong understanding of statistics, hypothesis testing, feature engineering, and model evaluation.
• Experience with model validation, cross‑validation, and performance metrics (MAPE, MAE, RMSE).
• Experience working with SQL and large‑scale datasets.
• Familiarity with data pipelines, experimentation frameworks, and analytical environments such as Jupyter or Databricks.
• Strong communication skills with the ability to present technical insights to business stakeholders.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

Responsibilities

• Design, develop, and deploy machine learning models for forecasting, prediction, and optimization use cases.
• Build and evaluate time-series forecasting solutions using statistical and machine learning approaches.
• Perform feature engineering including lag variables, rolling statistics, trend decomposition, seasonality analysis, and exogenous variable integration.
• Develop and benchmark models using techniques such as ARIMA, SARIMA, Prophet, XGBoost, Random Forest, Gradient Boosting, and Neural Networks.
• Conduct model validation, backtesting, and performance monitoring using appropriate evaluation metrics.
• Analyze large and complex datasets to identify trends, anomalies, and business opportunities.
• Perform exploratory data analysis (EDA) and statistical investigations.
• Design experiments and evaluate model performance using sound statistical methodologies.
• Build business-focused insights and recommendations from analytical findings.
• Collaborate with engineering teams to deploy machine learning solutions into production environments.
• Build scalable data pipelines and automated workflows.
• Implement model monitoring, drift detection, retraining strategies, and version control.
• Develop dashboards and reporting solutions using Power BI, Tableau, or Streamlit.
• Partner with business, product, and engineering stakeholders to define data-driven solutions.
• Communicate technical findings to both technical and non-technical audiences.
• Mentor junior team members and contribute to data science best practices.
• Participate in architecture discussions and support project delivery initiatives.

Education

Bachelor's or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

Tools

Python

Pandas

NumPy

Scikit-learn

XGBoost

ARIMA

SARIMA

Prophet

SQL

Jupyter

Databricks

AWS SageMaker

Azure ML

GCP AI

Job description

CodeNinja, a full‑stack AI delivery company, seeks a Senior Data Scientist (4+ years) in Lahore, Pakistan, to design, develop, and deploy forecasting and ML solutions for high‑impact business use cases. You will translate complex problems into scalable, production‑ready models that inform strategy and risk decisions.

You'll work on time‑series forecasting, feature engineering, model validation, and deploying pipelines, dashboards, and monitoring with cross‑functional teams across finance, risk,