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.
