Senior Data Scientist

CYNET SYSTEMS INC.

Washington, DC, United States

about 1 month ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

8 years minimum

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Artificial Intelligence Amazon Web Services Microsoft Azure Cloud Computing Graph Database Python (Programming Language) Machine Learning Open Source Technology Tensorflow Azure Machine Learning Pytorch Transfer Learning

+11 more

Large Language Models Snowflake Generative AI Containerization Scikit Learn Kubernetes Information Technology Data Management Machine Learning Operations Docker Databricks

Job description

Design, build, and optimize Generative AI, LLM, and multimodal foundation models for enterprise fintech applications.
Fine-tune or adapt open-source and proprietary models.
Build high-performance models for NLP, document intelligence, anomaly detection, risk scoring, predictive analytics, and decisioning use cases.
Lead experimentation to evaluate model accuracy, scalability, and fairness.
Partner with engineering teams to deploy models on cloud-based ML pipelines (Azure, AWS) & data platforms (Databricks & Snowflake).
Work with large-scale structured and unstructured datasets across the payments and financial ecosystem.
Implement model monitoring, drift detection, and continuous retraining strategies.
Evaluate and operationalize new AI technologies, foundation model architectures, responsible AI frameworks, and emerging research.
Drive POCs and innovation initiatives that enhance AI capabilities and differentiate our products.

Requirements

Master’s or PhD in Computer Science, Data Science, Machine Learning, AI, or related field.
8+ years of hands-on experience building and deploying machine learning models in production.
Proven expertise with LLMs, transformer architectures, transfer learning, and model fine-tuning.
Strong proficiency in Python, PyTorch or TensorFlow, and ML libraries such as Transformers.
Experience with cloud ML platforms, containerization (Docker/Kubernetes), and MLOps tools.
Solid understanding of statistical modeling, optimization, and evaluation methodologies.
Strong communication skills and ability to collaborate in cross-functional, fast-paced environments., * Experience working in fintech, payments, banking, or fraud/risk environments.
Background in vector databases, RAG pipelines, and knowledge graph integration.
Experience with data privacy, model governance, and Responsible AI frameworks.
Contributions to open-source AI/ML communities or research publications.