Senior Data Scientist

Precision Technologies; United States; On-site; USD 140,000 - 210,000; Full time

Job summary

Precision Technologies in the United States is seeking a Senior Data Scientist with 10+ years of experience in advanced analytics, modeling, and ML solution development. You will design, validate, and deploy scalable predictive and prescriptive models using data science, AI/ML, and cloud technologies.

The role requires strong Python, statistical analysis, ML algorithms, big data platforms, cloud ecosystems, MLOps practices, and business-driven analytics delivery.

Qualifications

• 10+ years in analytics, modeling, and ML solution development.
• Ability to design, validate, and deploy scalable predictive/prescriptive models.
• Strong Python, SQL, big data, and cloud ecosystem expertise.

Responsibilities

• Design, develop, and deploy ML and DL models for predictive analytics and forecasting.
• Build end-to-end data science pipelines from ingestion to deployment.
• Develop NLP models for text analytics when applicable.
• Implement MLOps, CI/CD, Docker, and Kubernetes for production models.
• Create dashboards to communicate insights to stakeholders.

Skills

Python

Statistical analysis

ML algorithms

Big data

Cloud platforms

MLOps

Data visualization

SQL

R

Tools

TensorFlow

PyTorch

Scikit-learn

XGBoost

Spark

Hadoop

Docker

Kubernetes

SageMaker

Job description

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

Experience: 10+ Years

Job Summary: We are seeking a highly experienced Senior Data Scientist with 10+ years of experience in advanced analytics, statistical modeling, and machine learning solution development. The ideal candidate will design, develop, validate, and deploy scalable predictive and prescriptive models using modern data science, AI/ML, and cloud technologies. This role requires strong expertise in Python, statistical analysis, machine learning algorithms, big data platforms, cloud ecosystems, MLOps practices, and business-driven analytics delivery.

Key Responsibilities:

• Design, develop, and deploy machine learning and deep learning models for predictive analytics, classification, regression, clustering, recommendation systems, and forecasting.
• Build end-to-end data science pipelines including data ingestion, feature engineering, model training, validation, and production deployment.
• Develop advanced analytics solutions using Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM).
• Perform exploratory data analysis (EDA) and statistical hypothesis testing using statistical techniques and probability modeling.
• Work extensively with SQL and RDBMS platforms to extract, transform, and analyze large datasets.
• Process and analyze large-scale datasets using big data technologies such as Spark, PySpark, or Hadoop ecosystems.
• Develop Natural Language Processing (NLP) models for text analytics, sentiment analysis, and language modeling where applicable.
• Implement model validation techniques, cross-validation, A/B testing, and performance evaluation metrics (ROC-AUC, precision-recall, RMSE, etc.).
• Deploy machine learning models into production using MLOps frameworks, CI/CD pipelines, Docker containers, and Kubernetes.
• Leverage cloud platforms (Azure, AWS, or GCP) for scalable model training, deployment, and monitoring.
• Develop and maintain automated model monitoring and drift detection frameworks.
• Create interactive dashboards and visualizations using Power BI, Tableau, or Matplotlib/Seaborn/Plotly to communicate insights to stakeholders.
• Collaborate with data engineers to optimize data pipelines and feature stores.
• Translate complex analytical results into actionable business insights for executive and non-technical stakeholders.
• Provide technical leadership, mentor junior data scientists, and contribute to enterprise AI strategy initiatives.

Required Skills & Qualifications:

• Advanced proficiency in Python for data science and machine learning development.
• Strong expertise in Machine Learning algorithms, supervised and unsupervised learning techniques.
• Experience with Deep Learning frameworks such as TensorFlow or PyTorch.
• Strong foundation in Statistics, Probability, Linear Algebra, and Predictive Modeling.
• Advanced SQL skills and experience working with enterprise RDBMS systems.
• Experience with Big Data platforms such as Spark or distributed computing frameworks.
• Hands-on experience deploying models using Docker, Kubernetes, and MLOps best practices.
• Experience working with cloud-based data science platforms (Azure ML, AWS SageMaker, or GCP AI Platform).
• Strong experience in data visualization and storytelling using BI tools.
• Experience working in Agile/Scrum environments and cross-functional teams.