Sr. Data Scientist

Data Collection and Preparation: Gathering unstructured and structured data from multiple sources, then cleaning and preprocessing it to ensure data quality and usability.

Exploratory Data Analysis (EDA): Analyzing data to uncover hidden patterns, correlations, and trends.

Modeling and Machine Learning: Developing algorithms, designing predictive models, and training them on historical data for tasks like classification, regression, and optimization.

Data Visualization and Reporting: Using tools (e.g., Tableau, Power BI) to create reports and charts that communicate technical findings to non-technical stakeholders.

Collaboration and Strategy: Working with business leaders to identify goals, define problems, and suggest data-driven improvements.

Model Maintenance: Testing, validating, and updating models to ensure accuracy and relevance over time.

Data Science, Python Programming/Scripting Language, R Programming Language, SQL (Structured Query Language), Mathematics, Statistics, Linear Algebra, Machine Learning, Algorithms, Data Collection, Programming: Proficiency in Python, R, and SQL.

Mathematics/Statistics: Strong understanding of statistical techniques, probability, and linear algebra.

Machine Learning: Knowledge of algorithms and libraries like Scikit-learn, TensorFlow, or PyTorch.