Senior Data Scientist

Role: Senior Data Scientist Locations: Bellevue, WA | Frisco, TX | Edison, NJ. Work Model: Hybrid Job Type: Full-Time Experience: 5+ Years strong experience on Machine learning implementation We are seeking a highly skilled and experienced Senior Data Scientist to lead and drive data science initiatives across the organization. This is a high-impact role requiring end-to-end ownership of the data science lifecycle-from business problem framing and data preparation to model development, deployment, and ongoing monitoring. The ideal candidate brings deep expertise in traditional machine learning and deep learning techniques, strong hands-on experience with Python and SQL, and a proven track record of building and deploying robust predictive models . The role also requires practical experience with Generative AI and OpenAI-based solutions , including chatbots, Retrieval-Augmented Generation (RAG), and AI-powered analytics use cases. Exposure to NLP is essential. Key Responsibilities Own the end-to-end development of data science solutions, including data wrangling, feature engineering, modeling, validation, deployment, and performance monitoring. Apply a wide range of traditional ML and DL algorithms-such as linear and logistic regression, decision trees, random forests, XGBoost, LightGBM, SVMs, clustering techniques, and deep neural networks-to solve complex business problems. Design and implement AI-driven solutions leveraging OpenAI models , including prompt engineering, RAG pipelines, and conversational AI systems for analytics and decision support. Collaborate closely with cross-functional stakeholders to translate business and marketing questions into analytical frameworks and scalable data solutions. Support campaign analytics and optimization by building models to measure campaign performance, engagement, uplift, and incremental impact across channels. Develop audience segmentation and persona models to support targeted campaign design, lifecycle analysis, and personalization strategies. Evaluate and forecast campaign outcomes (e.g., engagement, conversion, retention) using predictive and causal modeling approaches. Provide mentorship and technical leadership to junior data scientists and analysts, setting best practices for modeling and experimentation. Conduct rigorous model validation, monitor post-deployment performance, and iterate models to maintain accuracy and business relevance. Communicate insights, model outputs, and business impact clearly to both technical and non-technical stakeholders. Stay current with advancements in machine learning, generative AI, and applied analytics to continuously improve internal capabilities. Required Qualifications Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related quantitative field (PhD preferred). 5+ years of experience in applied data science with demonstrable project success and production deployments. Hands-on experience with OpenAI technologies and APIs for generative AI, NLP, or intelligent analytics use cases. Strong expertise in traditional machine learning techniques: Supervised learning: regression and classification (logistic regression, decision trees, random forest, gradient boosting, SVM, KNN) Unsupervised learning: clustering (k-means, DBSCAN), dimensionality reduction (PCA) Ensemble methods: bagging and boosting (XGBoost, LightGBM) Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL for large-scale data analysis and modeling. Experience deploying models into production and measuring business and campaign impact. Strong analytical thinking, clean coding practices, and ability to work independently and collaboratively. Excellent written and verbal communication skills. Good to Have Experience with NLP frameworks and libraries (spaCy, NLTK, Hugging Face Transformers). Advanced experience with Generative AI applications , including RAG architectures, embeddings, and vector databases. Familiarity with Azure cloud services , AI/ML tooling, and version control systems (Git). Understanding of MLOps principles , CI/CD for models, monitoring, and model governance.