Data Scientist

Responsibilities:

•    Analyze large and complex datasets to extract meaningful insights and trends.
•    Develop and implement machine learning models and algorithms for predictive and prescriptive analytics.
•    Design and execute experiments to test hypotheses and improve models.
•    Collaborate with cross-functional teams to identify business problems and provide data-driven solutions.
•    Develop data-driven strategies and recommendations to optimize business processes and decision-making.
•    Communicate findings and insights to non-technical stakeholders through visualizations, reports, and presentations.
•    Stay updated with the latest advancements in data science techniques, tools, and technologies.
•    Ensure data integrity, quality, and security throughout the data lifecycle.
•    Collaborate with data engineers and IT teams to ensure efficient data collection, storage, and processing.
•    Mentor and provide guidance to junior data scientists or analysts.

Requirements:

•    Proficiency in programming languages such as Python or R, along with libraries like TensorFlow, PyTorch, or scikit-learn.
•    Experience in working with relational databases, SQL, and data querying.
•    Strong understanding of statistical analysis, machine learning algorithms, and their applications.
•    Demonstrated experience in handling and analyzing large datasets using statistical and machine learning techniques.
•    Ability to preprocess and clean raw data for analysis, handling missing values, outliers, and feature engineering.
•    Proficiency in data manipulation, transformation, and analysis using Python, R, or similar tools.
•    Experience with implementing and evaluating various machine learning techniques, such as regression, classification, clustering, and deep learning.
•    Knowledge of data mining techniques and familiarity with tools like Spark or Hadoop.
•    Understanding of data visualization principles and experience in creating clear and informative visualizations.
•    Knowledge of data visualization tools like Tableau, Power BI, or matplotlib.
•    Familiarity with cloud platforms like AWS, Azure, or Google Cloud for data storage and processing.