Data Science Manager

Requirements

Strong background in machine learning, deep learning, and NLP, with proven experience in training and fine-tuning large-scale models (LLMs, transformers, diffusion models, etc. ).

Hands-on expertise with Parameter-Efficient Fine-Tuning (PEFT) approaches such as LoRA, prefix tuning, adapters, and quantization-aware training.

Proficiency in PyTorch, TensorFlow, Hugging Face ecosystem, and good to have distributed training frameworks (e. g., DeepSpeed, PyTorch Lightning, Ray).

Basic understanding of MLOps best practices, including experiment tracking, model versioning, CI / CD for ML pipelines, and deployment in production environments.

Experience working with large datasets, feature engineering, and data pipelines, leveraging tools such as Spark, Databricks, or cloud-native ML services (AWS Sagemaker, GCP Vertex AI or Azure ML).

Knowledge of GPU / TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments.

Applied Problem-Solving

Mandatory Skill

Demonstrated success in adapting foundation models to domain-specific applications through fine-tuning or transfer learning. Mandatory skill -

Strong ability to design, evaluate, and improve models using robust validation strategies, bias / fairness checks, and performance optimization techniques.

Experience in working on applied AI problems across NLP, computer vision, multimodal systems, or any other domain.

Leadership & Collaboration

Proven ability to lead and mentor a team of applied scientists and ML engineers, providing technical guidance and fostering innovation.

Strong cross-functional collaboration skills to work with product, engineering, and business stakeholders to deliver impactful AI solutions.

Ability to translate cutting-edge research into practical, scalable solutions that meet real-world business needs.

Other

Excellent communication and presentation skills to articulate complex ML concepts to both technical and non-technical audiences.

Continuous learner with awareness of emerging trends in generative AI, foundation models, and efficient ML techniques.

Education & Experience

Master's or Ph. D. in Computer Science, Machine Learning, Data Science, Statistics, or a related field.

7+ years of hands-on experience in applied machine learning and data science, with at least 2+ years in a leadership or managerial role.