Bloc Resources LLC
Data Scientist 1
Bloc Resources LLC, Atlanta, Georgia, United States, 30383
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.
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.