Why This Job is Featured on The SaaS Jobs
This Data Science Manager role stands out in the SaaS landscape because it centers on taking foundation models from research-grade capability into production-grade systems. The emphasis on LLMs, transformers, and parameter-efficient fine-tuning signals work that is increasingly core to SaaS differentiation, where product value is often delivered through embedded intelligence rather than standalone models.
From a career perspective, the remit spans the full arc that matters in modern SaaS AI: model adaptation to domain needs, rigorous evaluation and bias considerations, and the operational discipline of MLOps. Exposure to distributed training, cloud ML services, and GPU optimization builds fluency in the infrastructure constraints that shape real SaaS deployments. Leading applied scientists and ML engineers also develops the cross-functional translation skills needed to connect product goals with measurable model outcomes.
The position is best suited to experienced practitioners who enjoy balancing hands-on technical depth with team guidance and stakeholder alignment. It fits someone motivated by practical impact, comfortable navigating ambiguity in applied AI, and interested in building repeatable systems rather than one-off experiments. An on-site Bengaluru setup suggests close collaboration with engineering and product partners.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Technical Expertise
- 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, or 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.
