Job Description
Job Description:
Role:
Data Scientist (Multiple Openings) Location:
Jersey City, NJ / Atlanta, GA / Tampa, FL / Raleigh, NC / Denver , CO Job Type:
Full Time Job Description:
We are looking for a Data Scientist responsible for extracting insights from complex datasets and building data-driven solutions that drive business value. This includes everything from exploratory data analysis and statistical modeling to designing and deploying machine learning models in production. Your primary responsibility will be to design, develop, and deploy these models, and to coordinate with the rest of the team working on different layers of the data and analytics infrastructure. Thus, a commitment to collaborative problem solving, sound statistical reasoning, and delivering measurable business impact is essential. Need Experience range in between 2-7 Years Key Skill Requirements:
1. Strong knowledge of Python and/or R for data analysis, with hands-on experience using libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, and Keras. 2. Strong understanding of statistics, probability, hypothesis testing, A/B testing, and core ML concepts including regression, classification, clustering, ensemble methods (Random Forest, XGBoost), and neural networks. 3. Hands-on experience with end-to-end ML workflows including data wrangling, feature engineering, model training, hyperparameter tuning, evaluation, and deployment using Cloud Technologies (AWS SageMaker / Azure ML / Google Cloud Platform Vertex AI), MLflow, and Docker. 4. Strong experience with SQL and relational databases (MySQL, PostgreSQL), along with familiarity with big data tools (Spark, Hive) and data visualization libraries/tools (Matplotlib, Seaborn, Plotly, Tableau, or Power BI). Exposure to NLP, computer vision, or Generative AI / LLM
frameworks is a plus. Roles & Responsibilities:
1. Work closely with Team Leader, data engineers, product managers, and other team members to translate business requirements into analytical and machine learning solutions. 2. Get involved in the entire life-cycle including data exploration, model development, validation, deployment, and monitoring of models on the assigned delivery lines. 3. Do hands-on coding, build reproducible data pipelines, and perform rigorous model evaluation and testing. 4. Focus on clean, high-quality, well-documented code and follow established best practices in ML engineering and experimentation. 5. Learn new technologies and techniques in the rapidly evolving data science and AI space, implement them, and share learnings with other team members.
