Lead Time‑Series ML Engineer – Financial Forecasting

CodeNinja Inc.; Lahore; On-site; PKR 1,339,200 - 2,008,800; Full time

Benefits offered by this job

Provident Fund

Gym Membership

Leaves as per company policy

Company-paid trips

Yearly increment

Health insurance (maternity covered)

Job summary

CodeNinja Inc. in Lahore is looking for a Senior Machine Learning Engineer (Lead) to architect and develop advanced time-series forecasting solutions. Ideal candidates will have 5-6 years of experience in machine learning with a strong focus on financial applications. This role requires deep technical expertise, with responsibilities ranging from data acquisition to production-ready ML pipelines.

Join a dynamic team to work on impactful forecasting problems and drive innovation in financial decision-making.

Qualifications

• 5-6 years of hands-on ML engineering experience focused on time-series forecasting.
• Proven experience in financial domain use cases such as FX forecasting and treasury operations.
• Strong Python proficiency including libraries like pandas and XGBoost.

Responsibilities

• Lead end-to-end development of time-series forecasting solutions.
• Develop and benchmark statistical models and ML approaches.
• Collaborate with engineering teams to deploy solutions in cloud environments.

Skills

Machine Learning

Time-Series Forecasting

Financial Risk Modelling

Python (pandas, NumPy, scikit-learn)

Communication Skills

Education

Bachelor’s or Master’s in Computer Science, Data Science, Statistics

Tools

AWS SageMaker

Azure ML

GCP AI

Job description

CodeNinja Inc. in Lahore is looking for a Senior Machine Learning Engineer (Lead) to architect and develop advanced time-series forecasting solutions. Ideal candidates will have 5-6 years of experience in machine learning with a strong focus on financial applications. This role requires deep technical expertise, with responsibilities ranging from data acquisition to production-ready ML pipelines.

Join a dynamic team to work on impactful forecasting problems and drive innovation in financial decision-making.