Data Scientist

The Data Scientist at Pta plays a critical role in analyzing complex data sets to reveal actionable insights that drive strategic decisions. This position requires strong expertise in machine learning, statistical analysis, and data visualization, and involves working with large volume data using tools like Python, R, and SQL. The role demands proficiency with data manipulation libraries such as Pandas and NumPy, and experience in designing interactive dashboards using Tableau and Power BI. The Data Scientist collaborates with cross-functional teams to translate business needs into data-driven solutions, improving processes and outcomes through evidence-based decision making. This is an entry-level position requiring at least one year of relevant experience, with no team management responsibilities. In this role, the Data Scientist applies advanced analytical techniques including hypothesis testing and deep learning frameworks such as TensorFlow, Keras, and PyTorch to develop predictive models and neural networks. They ensure data integrity through rigorous data transformation and conduct statistical hypothesis testing to validate business assumptions. The role emphasizes continuous learning and adaptation of new data science methods and tools to keep pace with technological advancements. Effective communication of complex analysis through clear data visualizations and dashboards is vital for informing stakeholders at various levels.
Responsibilities

Collect, clean, and preprocess raw data from multiple sources to ensure data quality and consistency.
Perform exploratory data analysis using Python, R, and SQL to identify trends, patterns, and relationships.
Develop machine learning models to predict outcomes and improve decision-making processes.
Conduct statistical analysis and hypothesis testing to validate results and support strategic initiatives.
Build and deploy data visualization dashboards using Tableau, Power BI, and other tools to communicate insights effectively.
Manipulate and analyze data with libraries such as Pandas, NumPy, SciPy, and Statsmodels for advanced analytical calculations.
Design and implement deep learning models using TensorFlow, Keras, and PyTorch for complex data problems.
Collaborate with business stakeholders to understand requirements and translate them into actionable data science projects.
Continuously monitor and optimize the performance of models and dashboards to maintain accuracy and usability.
Maintain up-to-date documentation on data processes, models, and methodologies to ensure reproducibility and knowledge sharing.
Stay informed about developments in data science techniques and best practices to apply innovative solutions.
Support data transformation processes using Power Query and DAX expressions to structure data for analysis.
Create comprehensive reports integrating statistical findings and visualizations to support decision making.