JOB DESCRIPTION

• Work with stakeholders and development team to determine project scope and vision, gather and prioritize collected requirements 
• Provide technical support in developing, monitoring, and maintaining a high volume of complex data processes. 
• Data Collection: Collect, acquire, and gather data from various sources, which may include databases, APIs, web scraping, and more. 
• Data Cleaning: Clean, preprocess, and transform data to remove inconsistencies, missing values, and errors, ensuring that it is suitable for analysis.
• Data Exploration: Perform exploratory data analysis (EDA) to gain a deeper understanding of the data, identify patterns, and detect anomalies. 
• Data Modeling: Develop and implement machine learning and statistical models to solve specific business problems or make predictions. 
• Model Evaluation: Assess the performance of models using appropriate evaluation metrics and techniques, such as cross-validation and hyperparameter tuning. 
• Data Interpretation: Interpret the results of data analysis and modeling to provide actionable insights and recommendations to stakeholders. 
• Machine Learning: Apply various machine learning techniques, such as regression, classification, clustering, and deep learning, depending on the problem at hand. 
• Data Mining: Discover patterns and trends within large datasets to uncover valuable information. 
• Maintain procedures manuals and system documentation. 
• Research software solutions to support recommendations, analyzing current and future technology trends
• Data Manipulation: Experience in data manipulation tools and libraries like pandas, SQL, and data preprocessing techniques. 
• Model Evaluation: Expertise in evaluating machine learning models, conducting cross[1]validation, and hyperparameter tuning. 
• A/B Testing: Design and conduct experiments to measure the impact of changes and make data-driven recommendations. 
• Big Data Technologies: Familiarity with big data platforms and technologies such as Hadoop, Spark, and distributed computing. 
• Data Ethics: Knowledge of ethical considerations in data science, including privacy, bias, fairness, and regulatory compliance. 
• Ability to effectively prioritize and execute tasks in a fast-paced, high velocity, environment; ability to stay focused on responsibilities, meet deadlines and manage competing priorities under pressure. 
• Deep understanding of Agile methodologies and tools like Azure DevOps 
• Ability to understand customer requirements and translate these into solution specifications and system designs. 
• Ability to drive effective meetings: workshops, design, and problem-solving sessions.
• Ability to present ideas in business-friendly and user-friendly language; demonstrated aptitude for working with, supporting, and communicating with users and customers. 
• Excellent written and verbal communication skills, demonstrated initiative, strong work ethic, attention to detail, and a desire to succeed

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