Sr Data Scientist with Fraud Detection & GenAI & NLP - Hybrid on site
25 days ago
Washington
Sr Data Scientist with Fraud Detection & Time Series Analysis - Hybrid on site Minimum Qualifications: • Work or educational background at minimum of a Master's degree in one or more of the following areas: machine learning, computational linguistics, Fraud Analysis, Time series Analysis, 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., • 8-10 years of 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 FRAUD DETECTION and Time Series Analysis plus 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) Looking for "hands on" Data Scientist with Fraud detection and time series analysis • At least a Master's degree in Computer Science or any field related to AI., • Experience working with big data in AWS and using libraries such as PySpark., • Experience in time series forecasting and machine learning models., • Experience working with generative AI., • Experience working with log file analysis and tools such as Splunk. Qualifications & Requirements • Education: MS in Computer Science, Statistics, Math, Engineering, or related field,Master's required., • 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. Interview Process/# of Rounds: Interview Process/# of Rounds: • Direct manager contact, • 2 rounds