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.