Data Scientist (Exp in NLP and Gen AI) - Hybrid - Washington DC

Role: Data Scientist

Location: DC, 3 days onsite in Washington, DC

Exp: 12 yrs

• PhD in CS from a university in the states.
  
• Experienced in NLP and Gen AI.

Minimum Qualifications:

• Work or educational background in one or more of the following areas: machine learning, computational linguistics, deep learning, ratification intelligence, data science and/or data analytic, generative AI, symbolic AI, causal AI, operations research, computer science, Mathematics, business analytics, or knowledge management.
• Demonstrated experience programming with R/Python, Linux, and Spark in AWS cloud environment, or knowledge and algorithmic design experience in Python (3 years)
• Proficient with Amazon AWS Sagemaker, Jupyter Notebook and Python Scikit, Deep Learning, Machine Learning tools such as TensorFlow
• Experience with image processing models such as Coco, CLIP, ResNet or comparable models
• Demonstrated experience with machine learning techniques including natural language processing, and Large language Models (GPTv4-o1, o3, OpenAI APIs, Llama, Claude, etc).
• Experience developing AI agents and development proficiency using agentic programming
• Proficient in Natural language processing (NLP) and Natural language generation (NLG) including prior projects in any of the following categories: top modeling of text, sentiment analysis of text, part of speech tagging, Name Entity Recognition (NER), Bag of Words, text extraction
• Experience building and working with any of these components: Vector DB, BERT, RoBERTa (or comparable tools), Spacy, LLM and GenAI tools. Experience with LoRA, LangChain, RAG, LLM Fine Tuning and PEFT, Knowledge Graphs.
• Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement learning and AI development architectures with Human-in-the-Loop (HITL
• Demonstrated experience with SQL and any relational database technologies, such as Oracle, PostgreSQL, MySQL, RDS, Redshift, Hadoop EMR, Hive, etc.
• Demonstrated experience processing structured and unstructured data sources, data cleansing, data normalization and prep for analysis
• Demonstrated experience with code repositories and build/deployment pipelines, specifically Jenkins and/or Git/GitHub/GitLab.
• Demonstrated experience using Tableau, or Kibana, Quicksights or other similar data visualizations tools.
• Very comfortable working with ambiguity (e.g. imperfect data, loosely defined concepts, ideas, or goals)

Qualifications & Requirements

• Education: MS in Computer Science, Statistics, Math, Engineering, or related field, PhD preferred
• 3 years of relevant experience in building large scale machine learning or deep learning models and/or systems
• 1 year of experience specifically with deep learning (e.g., CNN, RNN, LSTM)
• 1 year of experience building NLP and NLG tools.
• Experience with wide range of LLMs (Llama, Claude, OpenAI, Cohere, etc.), LoRA, LangChain, RAG, LLM Fine Tuning and PEFT are preferred.
• Demonstrated skills with Jupyter Notebook, AWS Sagemaker, or Domino Datalab or comparable environments
• Passion for solving complex data problems and generating cross-functional solutions in a fast-paced environment
• Knowledge in Python and SQL, object-oriented programming, service oriented architectures
• Strong scripting skills with Shell script and SQL
• Strong coding skills and experience with Python (including SciPy, NumPy, and/or PySpark) and/or Scala.
• Knowledge and implementation experience with NLP techniques (topic modeling, bag of words, text classification, TF/IDF, Sentiment analysis) and NLP technologies such as Python NLTK, or Spacy or comparable technologies
• Knowledge and implementation experience with statistical and machine learning models (regression, classification, clustering, graph models, etc.)

Preferred Qualifications

• Hands on experience building models with deep learning frameworks like Tensorflow, Keras, Caffe, PyTorch, Theano, H2O, or similar
• Experience with LLM Agents, Agentic programming
• Experience with search architecture (for instance: Solr, ElasticSearch, AWS OpenSearch)
• Experience with building querying ontologies such as Zeno, OWL, RDF, SparQL or comparable are preferred
• Knowledge & experience with microservices, service mesh, API development and test automation are preferred
• Demonstrated experience using Docker, Kubernetes, and/or other similar container frameworks are preferred

Additional Job Qualifications:

• Ability to translate business ideas into analytics models that have major business impact.
• Demonstrated experience working with multiple stakeholders.
• Demonstrated communication skills, e.g. explaining complex technical issues to more junior data scientists, in graphical, verbal, or written formats.
• Demonstrated experience developing tested, reusable, and reproducible work.