Data Scientist

You will be responsible for conducting Exploratory Data Analysis (EDA) and data wrangling to clean, process, and visualize large datasets. Your primary task will involve developing and implementing Machine Learning models for demand forecasting using Python. Additionally, you will work on feature engineering to enhance model accuracy and performance. Handling time-series data processing for efficient forecasting will be a crucial part of your role. Collaborating with cross-functional teams is essential to deliver actionable insights from data analysis. You will utilize Excel for basic data manipulation tasks like pivoting, creating formulas (e.g., VLOOKUP, SUMIF), and performing aggregation functions. Working with SQL to query databases and write complex queries with JOIN, GROUP BY, WHERE clauses, and subqueries is a part of your daily responsibilities. Your main objective will be to provide solutions and insights for business forecasting within project timelines. Architecting and building end-to-end machine learning solutions, which include data extraction, feature engineering, model development, validation, deployment, and monitoring, based on statistical analysis, are critical aspects of the role. Developing and maintaining robust MLOps pipelines to automate and scale model deployment to ensure workflow reliability is also part of your duties. Applying statistical techniques such as hypothesis testing, A/B testing, and regression analysis to validate models and enhance decision-making are necessary. Your ability to translate complex business problems into analytical solutions using statistical reasoning and provide expert guidance on data science use cases will be valuable. Qualifications: Education: You should hold a Master's degree in Statistics, Mathematics, Computer Science, Data Science, or a related field. A PhD is preferred. Experience: A minimum of 7 years of experience as a Data Scientist or in a related role is required. Technical Skills: - Proficiency in Python and R programming languages - Experience with machine learning libraries like scikit-learn, TensorFlow, and PyTorch - Familiarity with data visualization libraries such as matplotlib, seaborn, and Tableau - Knowledge of big data technologies including Hadoop, Spark, and SQL - Experience with cloud computing platforms like AWS, Azure, or GCP is preferred Preferred Skills: - Experience with natural language processing (NLP) and deep learning techniques - Proficiency in time series analysis and forecasting - Knowledge of data mining and predictive modeling - Strong communication and presentation skills - Experience with Agile development methodologies Please note that the above qualifications and skills are essential for excelling in this position.,