Position: Data Scientist

Location: Remote (Washington, DC HQ – Hybrid option available)

Duration: 12 months with possible extension

Interview Process: 2 rounds (direct manager contact)

Background Check: Yes

Overview

We are seeking hands-on Data Scientists to support next-generation AI initiatives.

Two positions focus on Generative AI/NLP, and one focuses on Fraud Detection & Time Series Analysis.

We are looking for PhD-level (preferred) candidates who are passionate about applied AI, NLP, and advanced modeling — individuals who are hands-on builders, not leadership roles.

Key Responsibilities

Design and implement machine learning, deep learning, and AI models for real-world problems.
Develop and fine-tune Generative AI and NLP applications using LLMs (GPT-4, Claude, Llama, Mistral, etc.).
Apply RAG, LoRA, PEFT, and LangChain for retrieval augmentation and fine-tuning.
Work with Vector Databases, Knowledge Graphs, and Graph-based AI architectures.
Handle structured and unstructured data using PySpark, AWS SageMaker, and related tools.
Build and maintain CI/CD pipelines (Git, Jenkins, GitLab).
Collaborate with cross-functional teams to translate ideas into scalable production AI systems.

Minimum Qualifications

Education: MS in Computer Science, Statistics, Mathematics, or related field (PhD highly preferred).
3+ years of experience building and deploying ML/DL models.
Proficiency with:
Python (NumPy, SciPy, PySpark, Scikit-learn)
AWS SageMaker, Jupyter Notebooks
NLP tools: SpaCy, NLTK, BERT, RoBERTa, OpenAI APIs
Deep Learning frameworks: TensorFlow, PyTorch, Keras
Experience in Generative AI, NLP/NLG, and LLM Fine-Tuning.
Strong SQL and data pipeline development skills.
Familiar with data visualization tools like Tableau, Kibana, or QuickSight.

Preferred Qualifications

Experience with LLM Agents, Agentic Programming, and Human-in-the-Loop (HITL) systems.
Background in fraud detection, anomaly detection, or time series forecasting.
Experience with Docker, Kubernetes, ElasticSearch/OpenSearch.
Exposure to GraphRAG, Chain-of-Thought (CoT), and Knowledge Graphs (OWL, RDF, SPARQL).

Ideal Candidate

PhD or advanced MS with applied or published AI/ML research.
Hands-on, self-starter mindset with strong problem-solving and experimentation skills.
Passion for innovation in Generative AI, NLP, and predictive analytics.

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