Data Scientist

Quadlight Corp.; Charlotte (NC); On-site; USD 90,000 - 130,000; Full time

Job summary

Quadlight Corp Inc. is seeking a skilled Data Scientist to join our team in Charlotte, NC. You will apply data analysis and machine learning to solve business problems and drive data-driven decisions across cross-functional teams.

You will develop predictive models, interpret results, and deliver actionable recommendations while staying current with advances in data science and related fields.

Qualifications

• Master’s degree or Ph.D. in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field.
• Proven experience in data analysis, statistical modeling, and machine learning.

Responsibilities

• Analyze large, complex datasets to uncover patterns and trends.
• Develop machine learning models to solve business challenges and optimize processes.
• Apply statistical methods to interpret data and test hypotheses.
• Collaborate with stakeholders to translate requirements into analytical solutions.
• Clean, preprocess, and manipulate data for analysis and modeling.
• Design data-driven solutions to improve decision-making and efficiency.
• Communicate findings to non-technical stakeholders via reports and visuals.
• Evaluate model performance and refine algorithms for accuracy and scalability.
• Stay updated with advances in data science and ML.

Skills

Data analysis

Machine learning

Python

R

SQL

Education

Master’s degree or PhD in a quantitative field

Tools

TensorFlow

scikit-learn

pandas

Job description

Quadlight Corp Inc. is seeking a talented Data Scientist to join our dynamic team.

Company: Quadlight Corp Inc.

Location: Charlotte, NC

Job Type: Full-time

Number Of Openings: 1

Job Description:

As a Data Scientist, you will apply your expertise in data analysis and machine learning to solve complex business problems and drive data-driven decisions. You will work closely with cross-functional teams to extract insights from data, develop predictive models, and deliver actionable recommendations.

Responsibilities:

• Analyze large, complex datasets to uncover hidden patterns, correlations, and trends.
• Develop machine learning models and algorithms to solve business challenges and optimize processes.
• Apply statistical techniques and quantitative analysis methods to interpret data and test hypotheses.
• Collaborate with stakeholders to understand business requirements and translate them into analytical solutions.
• Clean, preprocess, and manipulate data for analysis and modeling purposes.
• Design and implement data-driven solutions that improve decision-making and operational efficiency.
• Communicate findings and insights to non-technical stakeholders through reports, visualizations, and presentations.
• Continuously evaluate model performance and refine algorithms to enhance accuracy and scalability.
• Stay updated with advancements in data science, machine learning, and related fields.

Requirements:

• Master’s degree or Ph.D. in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field.
• Proven experience as a Data Scientist or in a similar role, with expertise in data analysis, statistical modeling, and machine learning.
• Proficiency in programming languages and tools for data manipulation and analysis (e.g., Python, R, SQL).
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, scikit-learn, pandas).
• Strong understanding of statistical methods, hypothesis testing, and experimental design.
• Ability to work with large datasets and databases (SQL, NoSQL).
• Excellent problem-solving skills and attention to detail.
• Strong communication skills and ability to collaborate effectively with team members and stakeholders.

Preferred Qualifications:

• Experience with cloud platforms and services for data analysis and machine learning (e.g., AWS, Azure, Google Cloud).
• Knowledge of big data technologies and distributed computing frameworks (e.g., Hadoop, Spark).
• Previous experience in deploying machine learning models into production environments.
• Publications or contributions to data science projects, competitions, or open-source communities.