Senior Data Engineer

Job Description

• Develop scalable time series forecasting systems leveraging global models like Temporal Fusion Transformers, N-BEATS, and PATCHTST, enabling robust forecasting across thousands of retail and e-commerce time series.

• Foundational Knowledge on advanced regression.

• Apply generative AI to synthesize time series data, enhance model training pipelines, and build intelligent NLP-based assistants for analytics enablement.

• Design and train large-scale neural networks for time series, anomaly detection, and causal inference, with deployment via automated batch pipelines

• Build and maintain production-ready applications on cloud platforms, integrated with internal systems to deliver insights at scale.

Collaborate with cross-functional teams to define objectives, formulate hypotheses, and deploy models that directly influence decision-making.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to View email address on click.appcast.io learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

• Strong foundation in time series forecasting, deep learning, and optimization theory, with a focus on sequence modeling and attention-based architectures.

• Experience in training and deploying transformer-based neural networks (e.g., TFT, LSTM, GPT variants) for real-world business applications.

• Proficient in Python, SQL, and hands-on with PyTorch, TensorFlow/Keras, Scikit-learn, and statsmodels.

Demonstrated ability to work on GPU-based model training pipelines, and exposure to managing experimentation at scale. • Enterprise level experience preferred

• Deep Learning

• Agentic

• Gen AI

• Language Models