Data Scientist - Cognitive AI

About the Role

Key Responsibilities

• Design and implement scalable data-driven solutions for dynamic financial products.
• Deliver business insights from unstructured data with clear narratives and creative visualizations.
• Partner closely with data infrastructure and platform teams to develop tools and automation.
• Work collaboratively with product managers, data scientists, and engineers throughout the project lifecycle.
• Handle data extraction and preparation, including cleaning, lemmatization, and building high-quality corpora.
• Build and refine NLP and NLU models, including intent extraction, sentiment analysis, and query expansion.
• Train and iteratively improve intent classification systems using labeled data and user feedback.
• Apply statistical methodologies such as hypothesis testing, Bayesian inference, and experimental design to evaluate models.
• Design and deploy machine learning solutions, including classical algorithms and transformer-based architectures.
• Fine-tune and evaluate encoder models using techniques like LoRA, PEFT, quantization, and knowledge distillation.
• Apply Graph Neural Networks to build scalable recommendation systems and uncover relationships in data.
• Architect ML workflows using MLflow and integrate with CI/CD pipelines on cloud platforms.
• Optimize and scale training on GPU clusters using distributed training frameworks.
• Bridge the gap between technical outputs and business needs, translating analytics into actionable insights.
• Engage proactively with cross-functional teams to align AI/ML strategies with organizational goals.

Requirements

• Master's degree in Statistics, Mathematics, Computer Science, Operations Research, Engineering or similar quantitative field.
• 7+ years of experience in NLP/NLU, building both classical and transformer-based models (e.g., BERT, RoBERTa).
• 7+ years of experience with advanced proficiency in Python, Pytorch, Tensorflow, Hugging Face transformers and scikit-learn.
• 4+ years of deep understanding of transformer architectures, pretraining vs. fine-tuning, attention mechanisms and encoder models.
• 7+ years of experience in statistics, including A/B testing, hypothesis testing, and Bayesian methods.
• 7+ years of experience applying classical ML (e.g., XGBoost, Random Forests) and deep learning, including Graph Neural Networks.
• 4+ years of experience working with messy, unstructured, and multi-source text data and building high-throughput data processing pipelines.
• 2+ years of experience in model deployment using GCP, Kubernetes and model serving frameworks.
• 4+ years of experience using MLOps best practices, including versioning, monitoring, model drift detection, and retraining triggers.

Nice to Have

• Graduate degree in Statistics, Mathematics, Computer Science, Operations Research, Engineering or similar quantitative field.
• Experience with Agentic AI, LLM orchestration frameworks (Langchain, Llamaindex etc.).
• Exposure to Google Dialogflow or other conversational AI platforms.
• Experience in Dataiku, Streamlit, Flask or Dash for quick prototyping.
• Experience working in a regulated industry.
• Sharp business acumen, able to align technical solutions with strategic goals.
• Demonstrated leadership mindset, with a bias for action, critical thinking, and a deep dive mentality.

Qualifications

• Master's degree in a relevant quantitative field.
• 7+ years of experience in NLP/NLU and machine learning.

Benefits & Perks

• In addition to the salary range, this role is also eligible for bonus or incentive opportunities.