Why This Job is Featured on The SaaS Jobs
This Lead Data Scientist role stands out in the SaaS landscape because it sits at the intersection of production AI and modern foundation-model work. The emphasis on fine-tuning large models, PEFT techniques, and multimodal applications reflects how SaaS products are increasingly differentiating through embedded intelligence rather than standalone analytics. The listing also signals real-world constraints typical of SaaS delivery, including model efficiency, GPU optimization, and deployment-minded engineering.
From a career standpoint, the scope offers durable SaaS leverage: taking research-grade methods and translating them into repeatable capabilities that can be shipped, monitored, and improved over time. Exposure to experiment tracking, model versioning, and CI/CD for ML pipelines builds the operational muscle that many SaaS organizations need as they move from prototypes to reliable product features. Experience with distributed training and cloud ML stacks also transfers cleanly across SaaS companies modernizing their data and AI platforms.
This position is best suited to a senior practitioner who enjoys owning technical direction while staying close to implementation. It fits someone comfortable collaborating with product and engineering to define measurable outcomes, and who can mentor others without drifting into purely managerial work. It will appeal to candidates motivated by applied model performance, deployment realities, and iterative improvement cycles.
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.
- 5+ years of hands-on experience in applied machine learning and data science.
