Data Scientist

About Glia

Glia is the leading AI customer service solution for banks and credit unions. Our platform unifies AI and human agents across every voice and digital conversation through our proprietary ChannelLess® Architecture. With AI for All™, organizations overcome the tradeoff between efficiency and experience by using AI to automate conversations and elevate service operations.

Valued at over $1 billion and named a Deloitte Technology Fast 500™ company for five consecutive years, Glia powers over 700 financial institutions and maintains an industry-leading 72 NPS. We're also certified as a Great Place to Work, with 98% employee satisfaction.

The Role

We are looking for a Data Scientist who has a passion for cutting edge Conversational AI. You won’t just be "using" models; you’ll be the architect of how our LLM-based virtual assistants learn, adapt, and perform. You will bridge the gap between cutting-edge research and production-grade stability, ensuring our GVAs (Glia Virtual Assistants) provide fast, human-like, and—most importantly—hallucination-free interactions for our clients.

What You'll Do

• Architect & Maintain LLM Workflows: Design, implement, and optimize end-to-end pipelines for LLM-based chatbots, focusing on RAG (Retrieval-Augmented Generation) and agentic frameworks.

• Holistic Model Evaluation: Go beyond simple metrics. You will develop robust evaluation frameworks that measure:

  • Generative Output: Using LLM-as-a-judge, guardrails, and semantic similarity to ensure high-quality, on-brand responses.

  • Classifier Performance: Optimizing intent classification, sentiment analysis, and NER to ensure routing and logic remain flawless.

• Innovate with GVA Learning 360: Work on our proprietary technology that "clones" the wisdom of top human agents to automate complex banking scenarios.

• Applied Research: Act as a subject matter expert, staying ahead of the curve on breakthroughs in transformer architectures, reinforcement learning, prompt engineering and evaluation techniques.

• Identify Opportunities: Find areas where improvements can be made, such as additional capabilities (utilizing topic modelling), chaining models and agents to solve new workflows and increasing overall accuracy or reliability.

• Guardrail Engineering: Implement and refine safety layers to prevent hallucinations and ensure compliance within highly regulated industries (Finance).

What You Bring

• MSc. or PhD in computer science, mathematics, statistics, big data, or other data science disciplines

• 3-5 years experience working with large datasets and LLMs

• Product Focus with Feasibility Research & Strategic Analysis: Before diving into code, you perform rigorous feasibility studies. You can analyze the cost-benefit ratio of different approaches (e.g., "Is a specialized SLM more cost-effective than a general-purpose LLM for this specific task?") and assess the technical limitations of proposed products.

• Conversational AI Expertise: Proven experience building and deploying production-level conversational agents and NLP systems.

• LLM Proficiency: Deep knowledge of the LLM lifecycle—from prompt tuning and fine-tuning to managing context windows and latency optimization. Experience with Reinforcement Learning is a bonus.

• Evaluation Rigor: Experience with modern evaluation tools (e.g.,custom benchmarking suites or tools like langSmith) and traditional ML metrics (F1-score, Precision-Recall).

• A Researcher Mindset: You are driven by a problem-solving mindset, testing hypothesis, questioning existing solutions and driving to improve great solutions and products.

• Technical Stack: Expert-level Python, experience with frameworks like PyTorch/TensorFlow, and familiarity with vector databases (e.g. S3 Vectors or Pinecone) and LLM tools (e.g. Bedrock / Sagemaker).

• Data Preparation & Engineering: You possess a meticulous approach to data. You understand that the quality of a model is defined by the data it consumes. You are expert in

  • Cleaning and preprocessing unstructured conversational data (transcripts, chat logs).

  • Anonymizing Sensitive Personal Information (PII) to meet banking-grade security standards.

  • Curating high-quality "gold datasets" for fine-tuning and evaluation.

• Rapid Prototyping: You have a "bias for action." You can quickly translate a conceptual idea into a Proof of Concept (PoC) to demonstrate value to stakeholders before full-scale engineering begins.

• Database Expertise & SQL Mastery: You are a data explorer who doesn't need a middleman to access information.

  • SQL & Schemas: You have deep expertise in writing complex queries and designing efficient schemas for relational databases (PostgreSQL, MySQL).

  • Data Architecture: You understand how to structure data for your needs, ensuring that AI models can access history and context with sub-second latency.

Benefits

• Competitive salary and stock options

• Professional development support (trainings, courses, conferences, books, etc)

• Access to all the latest tools and equipment you’ll need

• Sports compensation, reimbursement for therapy, counseling sessions

• Team events: annual employee awards, internal hackathons, and a dozen cool events from cooking to the Glia olympic games :)

• Diversity: 25 countries represented

Glia is an equal-opportunity employer. Glia does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), or any other basis protected by law.

The Glia Talent Acquisition team uses @glia.com and @gliatalent.com email addresses for coordinating interviews, providing updates, and sending documents.

Our hiring process involves an introduction, practical and team interviews, and a decision and offer. For more information, visit our Recruitment Privacy Notice page or contact our talent team via [email protected]