Lead Data Scientist

E-Solutions; Washington (District of Columbia); On-site; USD 140,000 - 190,000; Full time

Job summary

E-Solutions in Washington, DC is seeking a Lead Data Scientist to architect, build, and deploy advanced ML and AI solutions powering payments, fraud detection, risk decisioning, and financial data enrichment. The role requires hands-on GenAI, NLP, and foundation models expertise, with a focus on enterprise-scale deployment and responsible AI practices.

You will collaborate with engineering on cloud pipelines (Azure, AWS) and data platforms (Databricks, Snowflake) to transform complex financial

Qualifications

• Hands-on expertise in Generative AI lifecycle.
• Strong NLP and Deep Learning acumen.
• Experience architecting enterprise-scale ML/AI solutions.
• Proven ability to deploy models in cloud pipelines.

Responsibilities

• Fine-tune or adapt open-source and proprietary models.
• Design, build, and optimize Generative AI, LLM, and foundation models for enterprise fintech applications.
• 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.

Skills

GenAI concepts

LLMs

NLP

Foundation models

MLOps

Fine-tuning

Tools

Azure

AWS

Databricks

Snowflake

Job description

Job Title: Lead Data Scientist

Location: Washington, DC (Onsite)

Long Term Contract

We are now explicitly seeking candidates with demonstrable, hands-on expertise in the Generative AI lifecycle, and also with strong NLP and Deep Learning acumen.

Since this shift significantly changes the interview landscape and screening process; I just wanted to align on the weight distribution: Should Traditional ML, Deep Learning, and GenAI concepts be weighted equally, or, given the nature of the new requirements, must we focus the majority of the evaluation on the candidate's ability on implementing GenAI solutions and enterprise-scale MLOps deployment ? Understanding this is vital as then there will be a need to restructure the interview questions at all levels, including the structure of our hands-on assessment.

Role Summary:

We are seeking a highly skilled Senior Data Scientist with expertise in Machine Learning, Artificial Intelligence, Large Language Models (LLMs), foundation models, and fine-tuning techniques. In this role, you will architect, build, and deploy advanced ML & AI solutions that power next-generation products across payments, fraud detection, risk decisioning, and financial data enrichment.

This position is ideal for someone who is passionate about building enterprise-grade AI & ML applications and applying cutting-edge techniques to real-world financial and payments challenges.

Key Responsibilities:

• Fine-tune or adapt open-source and proprietary models
• Design, build, and optimize Generative AI, LLM, and multimodal foundation models for enterprise fintech applications.
• 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.