Data Cleaning and Preprocessing: Perform thorough data cleaning, validation, and preprocessing to ensure the accuracy and reliability of the data used in analysis. Identify and handle missing data, outliers, and inconsistencies, maintaining data integrity throughout the pipeline.
Exploratory Data Analysis (EDA): Conduct comprehensive EDA to uncover patterns, trends, and correlations within the data. Apply statistical techniques and data visualization to communicate findings effectively.
Advanced Statistical Analysis: Perform advanced statistical analysis, including statistical testing and measuring statistical feature associations. Apply a wide range of statistical techniques to identify significant relationships, assess the impact of variables, and measure the strength of associations.
Modeling and Algorithm Development: Design and implement machine learning algorithms and statistical models to solve business problems and extract valuable insights from complex datasets. Collaborate with cross-functional teams to develop predictive models, optimize algorithms, and improve existing methodologies.
Building Forecasting Models: Utilize machine learning and deep learning techniques to develop accurate and reliable forecasting models. Apply time series analysis, regression, and other forecasting methodologies to predict future trends, demand, and outcomes.
Data Engineering: Develop and maintain scalable data pipelines, integrating and transforming data from various sources. Utilize data engineering techniques to streamline data acquisition, storage, and processing, ensuring efficient data flow across the organization.
Writing Optimized SQL Queries: Write efficient and optimized SQL queries for data analysis and retrieval. Utilize advanced SQL techniques to extract and manipulate data from relational databases, ensuring optimal performance and scalability.
Building Custom Live/Near Live Dashboards: Design and develop custom live/near live dashboards with complex functionality that integrate various data sources. Utilize business intelligence tools such as Tableau or Power BI to create interactive and visually appealing dashboards that enable real-time monitoring and analysis of key performance indicators.
Writing High-Quality and Organized Code: Ensure that code written for data cleaning, analysis, modeling, and engineering is of high quality, well-documented, and follows best practices. Write clean, efficient, and modular code that is easy to understand and maintain.
Communication and Visualization: Clearly communicate complex findings, methodologies, and insights to both technical and non-technical stakeholders. Visualize data and present results through compelling visualizations, dashboards, and reports.
Job Requirements
Bachelor's or master's degree in computer science, Statistics, Mathematics, or a related field.
Strong programming skills in Python and R. Proficiency in SQL is required.
Minimum of 3 years of experience in data science or a related field.
Solid understanding of statistical analysis, machine learning, and data mining techniques.
Experience with data cleaning, preprocessing, and exploratory data analysis.
Proficiency in utilizing data science libraries and frameworks (e.g., Pandas, NumPy, scikit-learn, TensorFlow, Py Torch).
Experience building forecasting models using machine learning and deep learning techniques.
Ability to perform advanced statistical analysis, including statistical testing and measuring statistical feature associations.
Proficiency in business intelligence tools such as Tableau or Power BI, with the ability to build custom live/near live dashboards with complex functionality.
Ability to write optimized SQL queries for data analysis and data retrieval.
