Data Scientist

Precision Technologies; United States; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Precision Technologies is seeking a data scientist to collect, clean, and analyze data from diverse sources to uncover trends and insights. You will apply EDA, statistical analysis, and feature engineering to build predictive models and ML pipelines.

The role involves deploying models, evaluating performance, and presenting findings via dashboards using Power BI, Tableau, or Python visuals. Collaboration with data engineers and product managers is essential.

Qualifications

• Experience collecting, cleaning, and analyzing large datasets from multiple sources.
• Strong Python data science toolkit with Pandas, NumPy, and SQL proficiency.
• Hands-on ML model development including training, evaluation, and deployment.

Responsibilities

• Design, develop, train, validate, and deploy ML models for various tasks.
• Build end-to-end ML pipelines with data prep, feature selection, and deployment.
• Collaborate with engineers and stakeholders to translate requirements into analytics solutions.
• Create dashboards and reports to communicate insights to business teams.

Skills

Python

Pandas

NumPy

SQL

EDA

Statistical Analysis

Feature Engineering

ML Pipelines

MLOps

Data Visualization

Tools

Power BI

Tableau

Matplotlib

Seaborn

Job description

Location: United States (Onsite / Hybrid / Remote - Based on Client Requirement)

Visa: Open to all work-authorized candidates except H1B

Key Responsibilities:

• Collect, clean, process, and analyze structured and unstructured data from multiple sources to identify trends, patterns, and actionable business insights.
• Perform Exploratory Data Analysis (EDA), statistical analysis, data profiling, data mining, and feature engineering using Python, Pandas, NumPy, and SQL.
• Design, develop, train, validate, and deploy Machine Learning models for classification, regression, clustering, forecasting, recommendation systems, and anomaly detection.
• Apply statistical modeling, probability, hypothesis testing, regression analysis, A/B testing, and time-series analysis to solve business problems.
• Build end-to-end Machine Learning pipelines covering data preparation, feature selection, model training, hyperparameter tuning, evaluation, and deployment.
• Develop predictive analytics, forecasting, optimization, and decision-support solutions to improve business performance and operational efficiency.
• Work with Deep Learning, Natural Language Processing (NLP), Computer Vision, Generative AI, Large Language Models (LLMs), and other advanced AI techniques as required.
• Query, transform, and analyze data using SQL, relational databases, cloud data warehouses, data lakes, and distributed data processing platforms.
• Evaluate model performance using appropriate metrics, cross-validation, explainability techniques, bias assessment, and statistical validation.
• Create data visualizations, dashboards, reports, and presentations using Power BI, Tableau, Matplotlib, Seaborn, or similar visualization tools.
• Collaborate with Data Engineers, ML Engineers, Business Analysts, Product Managers, and stakeholders to translate business requirements into measurable data science solutions.
• Deploy, monitor, and maintain models using MLOps practices, model versioning, automated pipelines, performance monitoring, and CI/CD workflows.

Contact:

Mahesh Kyadari, Lead - Talent Management Team