Data Scientist 1 - Bloc Resources LLC

Job Title: Data Scientist 1 
Location: Atlanta, GA (hybrid - must live within a commutable distance to Atlanta) 
Department: Quantitative Analytics (Business Development) 
Expected Duration of Assignment: 3 years 

Job Description

Job Summary: 
The Data Scientist is a highly skilled professional who uses data analysis and machine learning techniques to extract valuable insights and make data-driven decisions. They work with large datasets to solve complex problems and help organizations make informed choices.

Qualifications:

• 1 to 3 years of experience in data science, analytics, and modeling, preferably in the energy, natural gas, or finance industry. 
• Bachelors Degree plus 2-3 years of professional experience, or Masters Degree plus 1-2 years of experience. 
• Preferred majors are Mathematics, Economics, Engineering, Statistics, Computer Science, or similar discipline. 
• Preferred: Graduate degree in Quantitative discipline. 
• Professional experience in the natural gas, energy, or finance industry preferred. 
• Proven track record of model creation and management in a business environment.

Specific Skills & Knowledge:

• Knowledge of database management systems such as SQL Server, MySQL, or Microsoft Access.
• Experience in machine learning and data visualization: Strong experience in Python, Power BI, predictive modeling.
• Experience with SQL required. 
• Proven record of experience working with large databases and use of statistical models for valuation and prediction.
• Experience with Python required, experience with R or SPSS preferred but not required.
• Demonstrated quantitative skills and ability to apply complex financial and statistical principles.
• Ability to manage multiple projects at once with several departments and stakeholders in a timely fashion with measurable results.

General Responsibilities:

• Data Collection: Gather and collect large datasets from various sources, including databases, APIs, and external data providers.
• Data Cleaning: Preprocess and clean data to remove inconsistencies, missing values, and outliers to ensure data quality.
• Exploratory Data Analysis (EDA): Conduct exploratory data analysis to understand the characteristics and patterns within the data.
• Feature Engineering: Create relevant features or variables from raw data to improve the performance of machine learning models. Machine Learning
• Modeling: Develop and implement machine learning models to solve specific business problems, such as classification, regression, clustering, and recommendation systems.
• Model Evaluation: Assess the performance of machine learning models using various evaluation metrics and fine-tune them for optimal results.
• Data Visualization: Create clear and informative data visualizations and reports to communicate findings to non-technical stakeholders.
• Predictive Analytics: Use statistical and machine learning techniques to make predictions and forecast future trends.
• Statistical Analysis: Apply statistical methods to analyze data and test hypotheses.
• A/B Testing: Design and conduct A/B tests to evaluate the impact of changes and optimizations. Data Integration: Integrate data science solutions into existing software systems and workflows.
• Data Security: Ensure data privacy and security by implementing appropriate measures.
• Documentation: Maintain clear and organized documentation of data analysis processes, models, and findings. Continuous Learning: Stay up-to-date with the latest developments in data science and machine learning.

General Qualifications:

• Education: A bachelor's degree in a relevant field such as computer science, statistics, mathematics, or a related discipline. Many Data Scientists also hold master's or Ph.D. degrees.
• Programming Skills: Proficiency in programming languages such as Python or R is essential.
• Data Tools: Familiarity with data analysis and machine learning libraries and frameworks, such as Pandas, NumPy, ScikitLearn, TensorFlow, or PyTorch.
• Database Knowledge: Understanding of SQL and experience working with relational databases.
• Statistical Skills: Strong statistical knowledge and the ability to apply statistical techniques to real-world problems.
• Machine Learning: Expertise in machine learning algorithms and techniques, including supervised and unsupervised learning.
• Data Visualization: Proficiency in data visualization tools like Matplotlib, Seaborn, or Tableau.
• Problem Solving: Strong analytical and problem-solving skills to tackle complex, unstructured business challenges. Communication: Excellent communication skills to convey complex findings to non-technical stakeholders. Team Collaboration: Ability to work collaboratively in cross-functional teams.
• Domain Knowledge: Depending on the industry, domain-specific knowledge may be required (e.g., healthcare, finance, e-commerce).
• Ethical Considerations: Awareness of ethical considerations related to data handling and analysis, including privacy and bias. Data Scientists are instrumental in leveraging data to gain insights, improve decision-making, and drive innovation within organizations across various industries, including finance, healthcare, technology, and more. Their work contributes to business growth and competitiveness. 

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