Data Scientist

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.