Data Scientist, Senior

Data Scientist (LLM Specialist)

Key Responsibilities:

• LLM Development & Optimization:

  Train, fine-tune, evaluate, and deploy

  Large Language Models (LLMs)

  for various customer-facing applications.

• Pipeline & Workflow Development:

  Build scalable

  machine learning workflows and pipelines

  that facilitate efficient data ingestion, model training, and deployment.

• Model Evaluation & Performance Tuning:

  Implement best-in-class

  evaluation metrics

  to assess model performance, optimize for efficiency, and mitigate biases in LLM applications.

• Customer Engagement:

  Collaborate closely with customers to understand their needs,

  design AI-driven solutions

  , and iterate on models to enhance user experiences.

• Research & Innovation:

  Stay updated on the latest developments in LLMs,

  agentic AI

  , reinforcement learning with human feedback (RLHF), and generative AI applications. Recommend

  novel approaches

  to improve AI-based solutions.

• Infrastructure & Deployment:

  Work with

  MLOps tools

  to streamline deployment and serve models efficiently using cloud-based or on-premise architectures, including

  Google Vertex AI

  for model training, deployment, and inference.

• Foundational Model Training:

  Experience working with

  open-weight foundational models

  , leveraging pre-trained architectures, fine-tuning on domain-specific datasets, and optimizing models for performance and cost-efficiency.

• Cross-Functional Collaboration:

  Partner with

  engineering, product, and design teams

  to integrate LLM-based solutions into customer products seamlessly.

• Ethical AI Practices:

  Ensure responsible AI development by addressing concerns related to

  bias, safety, security, and interpretability

  in LLMs.

• Experience:

  experience in ML, NLP, or AI-related roles, with a focus on

  LLMs and generative AI

  .

• Programming Skills:

  Proficiency in

  Python

  and experience with ML frameworks like

  TensorFlow, PyTorch

• LLM Expertise:

  Hands-on experience in training, fine-tuning, and deploying LLMs (e.g., OpenAI’s GPT, Meta’s LLaMA, Mistral, or other transformer-based architectures).

• Foundational Model Knowledge:

  Strong understanding of

  open-weight LLM architectures

  , including

  training methodologies, fine-tuning techniques, hyperparameter optimization, and model distillation

  .

• Data Pipeline Development:

  Strong understanding of

  data engineering concepts

  , feature engineering, and workflow automation using

  Airflow or Kubeflow

  .

• Cloud & MLOps:

  Experience deploying ML models in cloud environments like

  AWS, GCP (Google Vertex AI), or Azure

  using

  Docker and Kubernetes

  .

• Model Serving & Optimization:

  Proficiency in

  model quantization, pruning, distillation, and knowledge distillation

  to improve deployment efficiency and scalability.

• Research & Problem-Solving:

  Ability to conduct

  independent research

  , explore

  novel solutions

  , and implement state-of-the-art ML techniques.

• Strong Communication Skills:

  Ability to

  translate technical concepts

  into actionable insights for non-technical stakeholders.

• Version Control & Collaboration:

  Proficiency in

  Git, CI/CD pipelines

  , and working in

  cross-functional teams

  .

• Bachelor’s in Computer Science, Machine learning, or related discipline.Master’s preferred
• Strong background in statistics, machine learning, deep learning and programming necessary. 5+years experience required
• Experience in solving large-scale real-world industry problems, preferably in collaboration with cross-functional, multi-disciplinary teams
• Knowledge of statistical programming techniques and languages (e.g., R, Python, Java, etc.)
• Working knowledge of common machine learning and deep learning approaches (e.g. regression, clustering, classification, dimensionality reduction, supervised and unsupervised techniques, Bayesian reasoning, boosting, random forests, deep learning) and data analysis packages (e.g. scikit-learn, pyclustering, pathways analysis, MLlib)
• Prior experience with Tensorflow
• Prior experience in Natural Language Processing using NLTK
• Retail industry experience desired
• Experience using cloud compute (e.g. Google Cloud Platform, AWS, Azure)
• Familiarity with NoSQL databases, graphical analyses, and large-scale data processing frameworks (e.g. Apache Spark)
• Solid understanding of data structures, software design and architecture
• Ability to work independently and take initiative, but also a co-operative team player