Data Scientist 2 4P/187

4P Consulting Inc.; Atlanta (GA); On-site; USD 80,000 - 120,000; Full time

Job summary

An established industry player is seeking a skilled Data Scientist with 5 to 10 years of experience to drive data-driven decision-making through advanced analytics and machine learning. This role involves collecting and analyzing complex datasets, developing predictive models, and creating compelling visualizations to communicate insights effectively. The ideal candidate will possess strong problem-solving abilities and a solid understanding of data ethics. Join a dynamic team that values continuous learning and mentorship while contributing to impactful data initiatives that shape business strategies.

Qualifications

• 5-10 years of experience in data science with a focus on machine learning and analytics.
• Bachelor’s in Computer Science, Statistics, or related field; advanced degrees are a plus.

Responsibilities

• Analyze complex datasets to derive insights and support business strategies.
• Develop machine learning models and deploy them for real-time analytics.

Skills

Machine Learning

Data Analysis

Statistical Analysis

Problem-Solving

Data Visualization

Hypothesis Testing

Feature Engineering

A/B Testing

Communication Skills

Education

Bachelor’s degree in a quantitative field

Master’s or Ph.D.

Tools

Python

R

Julia

Tableau

Power BI

Matplotlib

Seaborn

SQL

Hadoop

Spark

Job description

Data Scientist (5–10 Years Experience)

Overview:

A Data Scientist with 5 to 10 years of experience is responsible for leveraging data to uncover insights, create predictive models, and drive data-driven decision-making within an organization. This role requires advanced analytics, machine learning expertise, and strong problem-solving skills to extract actionable intelligence from large and complex datasets.

Key Responsibilities:

1. Data Analysis:

1. Collect, clean, and analyze complex datasets to uncover trends, patterns, and actionable insights.
2. Apply statistical techniques to derive meaningful information for business strategies.

2. Predictive Modeling:

1. Develop and deploy machine learning models to forecast future trends, behaviors, and outcomes.
2. Utilize techniques such as regression analysis, classification, and clustering.

3. Data Visualization:

1. Create compelling visualizations using tools like Tableau, Power BI, and Python libraries (e.g., Matplotlib, Seaborn).
2. Effectively communicate insights to both technical and non-technical stakeholders.

4. Hypothesis Testing:

1. Formulate and test hypotheses to statistically validate business decisions and recommendations.

5. Feature Engineering:

1. Engineer and select relevant features to optimize the performance of machine learning models.

6. Algorithm Development:

1. Build and fine-tune machine learning algorithms such as decision trees, random forests, and neural networks.

7. Data Integration:

1. Collaborate with IT and database administrators to access and integrate data from multiple sources and data warehouses.

8. Model Deployment:

1. Deploy machine learning models into production environments to support real-time analytics and decision-making.

9. A/B Testing:

1. Design and evaluate A/B tests to assess the impact of process or product changes.

10. Data Ethics:

1. Ensure data handling practices meet ethical standards, including privacy and compliance with regulations.

11. Cross-functional Collaboration:

1. Work closely with engineers, business analysts, and domain experts to align data initiatives with business goals.

12. Mentorship:

1. Provide guidance and mentorship to junior data scientists and analysts to support team development.

13. Continuous Learning:

1. Stay updated on the latest data science tools, trends, and best practices through professional development.

Qualifications:

1. Education: Bachelor’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering).
   Master’s or Ph.D. is a plus.
2. Experience: 5 to 10 years in data science, with experience in machine learning and statistical analysis.
3. Programming Languages & Tools: Proficiency in Python, R, or Julia.
4. Visualization Tools: Experience with Tableau, Power BI, and Python visualization libraries (Matplotlib, Seaborn).
5. Database Skills: Strong understanding of databases and SQL-based data manipulation.
6. Additional Skills:
   1. Advanced problem-solving and critical thinking abilities.
   2. Strong communication skills for conveying technical findings to diverse audiences.
   3. Familiarity with big data and distributed computing frameworks (e.g., Hadoop, Spark) is a plus.
   4. Awareness of data ethics and regulatory compliance.