AVP Data Science

Summary

We are seeking a highly skilled and motivated Data Scientist to join our team. The ideal candidate will have a strong analytical mindset, exceptional problem-solving skills, and the ability to transform data into actionable insights. As a Data Scientist, you will work on diverse datasets, develop predictive models, and provide data-driven solutions to support business decisions.

Responsibilities:

• Collect, clean, and preprocess structured and unstructured data from various sources.
• Apply statistical techniques and machine learning algorithms to analyze data and develop predictive models.
• Create interactive dashboards and visualizations to present insights to stakeholders.
• Communicate complex findings in a clear and concise manner.
• Design and implement scalable algorithms for data analysis, prediction, and optimization.
• Continuously improve existing models for better accuracy and performance.
• Work closely with cross-functional teams, including engineering, product, and marketing, to understand business needs.
• Contribute to the development of data strategies to achieve organizational goals.
• Stay updated on the latest trends and advancements in data science and machine learning.
• Experiment with new tools and technologies to enhance analytical capabilities.

Requirements:

• Bachelor’s/Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Proficiency in programming languages such as Python, R, or SQL.
• Experience with data visualization tools like Tableau, Power BI, or Matplotlib.
• Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Familiarity with big data tools like Hadoop, Spark, or AWS is a plus.
• Strong understanding of statistical analysis and predictive modeling.
• Ability to identify patterns, trends, and correlations in large datasets.
• Plus years of experience in data science, machine learning, or a related field.
• Demonstrated experience in handling large datasets and solving real-world problems.