Data Scientist 2 4P/187
Apply NowCompany: 4P Consulting Inc.
Location: Atlanta, GA 30349
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:
2. Predictive Modeling:
3. Data Visualization:
4. Hypothesis Testing:
5. Feature Engineering:
6. Algorithm Development:
7. Data Integration:
8. Model Deployment:
9. A/B Testing:
10. Data Ethics:
11. Cross-functional Collaboration:
12. Mentorship:
13. Continuous Learning:
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:
- Collect, clean, and analyze complex datasets to uncover trends, patterns, and actionable insights.
- Apply statistical techniques to derive meaningful information for business strategies.
2. Predictive Modeling:
- Develop and deploy machine learning models to forecast future trends, behaviors, and outcomes.
- Utilize techniques such as regression analysis, classification, and clustering.
3. Data Visualization:
- Create compelling visualizations using tools like Tableau, Power BI, and Python libraries (e.g., Matplotlib, Seaborn).
- Effectively communicate insights to both technical and non-technical stakeholders.
4. Hypothesis Testing:
- Formulate and test hypotheses to statistically validate business decisions and recommendations.
5. Feature Engineering:
- Engineer and select relevant features to optimize the performance of machine learning models.
6. Algorithm Development:
- Build and fine-tune machine learning algorithms such as decision trees, random forests, and neural networks.
7. Data Integration:
- Collaborate with IT and database administrators to access and integrate data from multiple sources and data warehouses.
8. Model Deployment:
- Deploy machine learning models into production environments to support real-time analytics and decision-making.
9. A/B Testing:
- Design and evaluate A/B tests to assess the impact of process or product changes.
10. Data Ethics:
- Ensure data handling practices meet ethical standards, including privacy and compliance with regulations.
11. Cross-functional Collaboration:
- Work closely with engineers, business analysts, and domain experts to align data initiatives with business goals.
12. Mentorship:
- Provide guidance and mentorship to junior data scientists and analysts to support team development.
13. Continuous Learning:
- Stay updated on the latest data science tools, trends, and best practices through professional development.
- Education: Bachelor's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering).
Master's or Ph.D. is a plus. - Experience: 5 to 10 years in data science, with experience in machine learning and statistical analysis.
- Programming Languages & Tools: Proficiency in Python, R, or Julia.
- Visualization Tools: Experience with Tableau, Power BI, and Python visualization libraries (Matplotlib, Seaborn).
- Database Skills: Strong understanding of databases and SQL-based data manipulation.
- Additional Skills:
- Advanced problem-solving and critical thinking abilities.
- Strong communication skills for conveying technical findings to diverse audiences.
- Familiarity with big data and distributed computing frameworks (e.g., Hadoop, Spark) is a plus.
- Awareness of data ethics and regulatory compliance.