Senior Data Scientist

Job title: Senior Data Scientist

Location: Texas (Onsite)

Employment Type: Full-time (W2 only, No C2C)

Experience: 10 Years

Job Summary: We are seeking a highly experienced Data Scientist with 10 years of professional experience in building advanced machine learning models, statistical analysis, and data-driven solutions for enterprise applications. The ideal candidate will have strong expertise in data science, predictive modeling, big data analytics, data engineering, and cloud-based AI/ML platforms. The candidate will work closely with data engineers, business stakeholders, and product teams to deliver scalable and impactful data solutions while following modern MLOps, Agile, and DevOps practices.

Key Responsibilities:

• Design, develop, and deploy machine learning and deep learning models using Python, R, Scikit-learn, TensorFlow, PyTorch, and Keras, enabling predictive analytics and intelligent decision-making.
• Perform data preprocessing, feature engineering, and exploratory data analysis (EDA) using Pandas, NumPy, and statistical techniques, ensuring high-quality datasets for modeling.
• Build and optimize supervised and unsupervised learning models such as regression, classification, clustering, and recommendation systems, improving model accuracy and performance.
• Develop and implement natural language processing (NLP) and text analytics solutions using NLTK, spaCy, and transformer-based models, enabling insights from unstructured data.
• Design and implement big data analytics solutions using Apache Spark, PySpark, Hadoop, and distributed computing frameworks, handling large-scale datasets efficiently.
• Deploy and manage machine learning models in production using MLOps practices, Docker, Kubernetes, and CI/CD pipelines, ensuring scalability and reliability.
• Integrate machine learning solutions with cloud platforms such as AWS (SageMaker), Microsoft Azure ML, or Google Cloud AI/ML services, enabling cloud-native data science workflows.
• Develop and maintain data pipelines and workflows in collaboration with data engineering teams using ETL/ELT processes and tools, ensuring seamless data integration.
• Perform statistical analysis and hypothesis testing using techniques such as A/B testing, regression analysis, and probability modeling.
• Create data visualizations and dashboards using tools such as Tableau, Power BI, Matplotlib, Seaborn, or Plotly, enabling business insights and decision-making.
• Implement model evaluation, validation, and monitoring techniques to ensure model performance, drift detection, and continuous improvement.
• Work with SQL and NoSQL databases such as MySQL, PostgreSQL, MongoDB, and data warehouses, ensuring efficient data retrieval and storage.
• Collaborate with cross-functional Agile teams, including engineers, analysts, and stakeholders, to define data-driven solutions aligned with business goals.
• Apply data governance, data privacy, and ethical AI practices, ensuring compliance with regulations and standards.
• Mentor junior data scientists and contribute to best practices in machine learning, data science, and model deployment strategies.

Technical Skills:

• Programming Languages: Python, R, SQL
• Machine Learning Frameworks: Scikit-learn, TensorFlow, PyTorch, Keras
• Data Processing Libraries: Pandas, NumPy
• Big Data Technologies: Apache Spark, PySpark, Hadoop
• NLP Tools: NLTK, spaCy, Transformers
• Data Visualization: Tableau, Power BI, Matplotlib, Seaborn, Plotly
• Cloud Platforms: AWS (SageMaker), Microsoft Azure ML, Google Cloud AI/ML
• MLOps & Deployment: Docker, Kubernetes, CI/CD Pipelines
• Databases: MySQL, PostgreSQL, MongoDB, Data Warehouses
• Statistical Techniques: Regression, Classification, Clustering, A/B Testing
• Version Control: Git, GitHub, Bitbucket, GitLab
• ETL Tools: Apache Airflow, Azure Data Factory
• Operating Systems: Linux, Windows
• Methodologies: Agile, Scrum, DevOps, MLOps