Why This Job is Featured on The SaaS Jobs
This Data Science Manager role stands out in the SaaS landscape because it sits at the intersection of productized AI and production delivery. The description signals work on adapting foundation models to domain use cases, with an emphasis on efficient fine-tuning methods and deployment-aware engineering. That combination is increasingly central for SaaS companies that need to translate model capability into reliable features, not just research outcomes, while managing cost and latency constraints.
From a SaaS career perspective, the remit builds durable experience across the full applied ML lifecycle: data pipelines, experimentation discipline, model governance, and operationalization through MLOps practices. Exposure to distributed training frameworks and cloud or HPC optimization also maps well to how modern SaaS teams scale model development and iterate toward measurable product impact. The leadership component adds a second track of growth by formalizing technical direction-setting and stakeholder translation.
This position is best suited to a manager who still prefers hands-on technical depth and can guide a team through ambiguous model selection, evaluation rigor, and production trade-offs. It will fit someone motivated by applied generative AI work where success depends on collaboration with product and engineering and on turning research advances into maintainable systems.
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.
