Data Scientist, II

Data Scientist (LLM Specialist)

• 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.

• 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 degree. Advanced degree–masters or PhD-strongly preferred in Statistics, Mathematics, Data / Computer Science or related discipline
• 2-5 years experience
• Statistics modeling and algorithms
• Machine Learning Experience–including deep learning and neural networks, genetics algorithm etc.
• Working knowledge Big Data–Hadoop, Cassandra,Spark R. Hands-on experience preferred
• Data Mining
• Data Visualization and visualization and analysis tools including R
• Work/Project experience in sensors, IoT, mobile industry highly preferred
• Excellent verbal and written communication
• Comfortable with presenting to senior management and CxO level executives
• Self motivated and self starter with high degree of work ethic