Data Scientist

Overview

On Site
35 - 45
Contract - Independent
Contract - W2
Contract - 12 Month(s)
No Travel Required
Able to Provide Sponsorship

Skills

Algorithms
Business Intelligence
Apache Hadoop
Apache Spark
Machine Learning (ML)
Microsoft Power BI
Data Engineering
Dashboard
Data Science
Pandas
NumPy
Machine Learning Operations (ML Ops)
PyTorch
Python
TensorFlow

Job Details

Data Scientist

Location: NJ, NY, CT, PA, DE

We are seeking a highly skilled Data Scientist to design, build, and deploy data-driven solutions that support business decision-making. The ideal candidate will have strong analytical abilities, expertise in machine learning, and hands-on experience working with large datasets and cloud platforms.

Key Responsibilities

  • Collect, clean, and analyze structured and unstructured datasets to extract actionable insights.

  • Build, train, and optimize machine learning and statistical models for prediction, classification, clustering, and NLP tasks.

  • Develop scalable data pipelines and model deployment workflows in collaboration with data engineering teams.

  • Translate business problems into analytical solutions and present results to non-technical stakeholders.

  • Perform A/B tests, statistical experiments, and deep-dive analyses to support business strategy.

  • Create dashboards, visualizations, and automated reports using BI and analytics tools.

  • Deploy models into production using MLOps tools and monitor model performance and data drift.

  • Research and implement new algorithms, tools, and data science best practices.

Required Skills

  • Strong programming skills in Python (NumPy, Pandas, Scikit-learn, Matplotlib, TensorFlow/PyTorch preferred).

  • Experience with machine learning, statistical modeling, feature engineering, and data preprocessing.

  • Expertise in SQL and experience with large-scale data (Spark, Databricks, Hadoop, Snowflake, BigQuery, etc.).

  • Knowledge of data visualization tools such as Tableau, Power BI, or Plotly.

  • Experience building end-to-end ML solutions from exploration to production.

  • Familiarity with MLOps, model deployment, CI/CD, and version control (Git).

  • Strong understanding of probability, statistics, and experimental design.

  • Excellent communication and problem-solving skills.

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