Data Scientist (US)

EdgeQ Technologies•New York City, NY

About The Position

The Data Scientist transforms complex datasets into actionable business insights. The role analyzes business performance, develops predictive models, identifies trends, and supports data-driven decision-making in collaboration with cross-functional teams. It involves working with structured and unstructured data, applying statistical analysis and machine learning, and presenting findings through dashboards, reports, and visualizations. The position is available in New York City, Boston, Seattle, or Chicago.

Requirements

1–5 years of professional experience in data science, analytics, machine learning, or a related field.
Strong programming skills in Python.
Proficiency in SQL and relational database querying.
Solid understanding of statistics, probability, hypothesis testing, and experimental design.
Experience with machine learning algorithms, model training, and model evaluation.
Familiarity with pandas, NumPy, scikit-learn, and other Python data science libraries.
Experience creating dashboards and visual reports using Tableau.
Knowledge of data cleaning, feature engineering, and exploratory data analysis.
Ability to interpret complex data and translate findings into actionable business insights.
Strong problem-solving, critical-thinking, and communication skills.
Ability to manage multiple analytical tasks and collaborate effectively with cross-functional teams.

Nice To Haves

Experience with Power BI or other business intelligence and visualization platforms.
Familiarity with cloud data platforms such as AWS, Microsoft Azure, or Google Cloud.
Knowledge of data warehouses and platforms such as Snowflake, BigQuery, or Redshift.
Experience with A/B testing, forecasting, customer segmentation, or predictive analytics.
Familiarity with Git, Jupyter Notebook, and collaborative development workflows.
Understanding of ETL processes, data pipelines, and data quality management.
Experience applying machine learning to business use cases such as customer churn, fraud detection, recommendation systems, or demand forecasting.

Responsibilities

Collect, clean, process, and analyze structured and unstructured datasets from multiple sources.
Develop statistical models and machine learning solutions to solve business problems.
Use Python libraries such as pandas, NumPy, scikit-learn, and SciPy for data analysis and modeling.
Write optimized SQL queries to extract, transform, and analyze data from relational databases.
Perform exploratory data analysis to identify patterns, trends, correlations, and anomalies.
Apply statistical methods, hypothesis testing, and experimental analysis to support business decisions.
Build, validate, and evaluate predictive models using appropriate performance metrics.
Create interactive dashboards, reports, and visualizations using Tableau.
Communicate analytical findings and recommendations to technical and non-technical stakeholders.
Collaborate with data engineers, business analysts, product managers, and engineering teams.
Monitor model performance and maintain analytical workflows as business requirements evolve.
Document methodologies, assumptions, results, and recommendations.