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

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• Own the end-to-end development of data science solutions, including data wrangling, feature engineering, modeling, validation, deployment, and performance monitoring.
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• 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.
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• Design and implement AI-driven solutions leveraging OpenAI models, including prompt engineering, RAG pipelines, and conversational AI systems for analytics and decision support.
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• Collaborate closely with cross-functional stakeholders to translate business and marketing questions into analytical frameworks and scalable data solutions.
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• Support campaign analytics and optimization by building models to measure campaign performance, engagement, uplift, and incremental impact across channels.
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• Develop audience segmentation and persona models to support targeted campaign design, lifecycle analysis, and personalization strategies.
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• Evaluate and forecast campaign outcomes (e.g., engagement, conversion, retention) using predictive and causal modeling approaches.
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• Provide mentorship and technical leadership to junior data scientists and analysts, setting best practices for modeling and experimentation.
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• Conduct rigorous model validation, monitor post-deployment performance, and iterate models to maintain accuracy and business relevance.
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• Communicate insights, model outputs, and business impact clearly to both technical and non-technical stakeholders.
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• Stay current with advancements in machine learning, generative AI, and applied analytics to continuously improve internal capabilities.
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Required Qualifications

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• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related quantitative field (PhD preferred).
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• 5+ years of experience in applied data science with demonstrable project success and production deployments.
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• Hands-on experience with OpenAI technologies and APIs for generative AI, NLP, or intelligent analytics use cases.
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• Strong expertise in traditional machine learning techniques:
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• Supervised learning: regression and classification (logistic regression, decision trees, random forest, gradient boosting, SVM, KNN)
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• Unsupervised learning: clustering (k-means, DBSCAN), dimensionality reduction (PCA)
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• Ensemble methods: bagging and boosting (XGBoost, LightGBM)
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• Proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL for large-scale data analysis and modeling.
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• Experience deploying models into production and measuring business and campaign impact.
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• Strong analytical thinking, clean coding practices, and ability to work independently and collaboratively.
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• Excellent written and verbal communication skills.
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Good to Have

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• Experience with NLP frameworks and libraries (spaCy, NLTK, Hugging Face Transformers).
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• Advanced experience with Generative AI applications, including RAG architectures, embeddings, and vector databases.
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• Familiarity with Azure cloud services, AI/ML tooling, and version control systems (Git).
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• Understanding of MLOps principles, CI/CD for models, monitoring, and model governance.
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