Data Scientist

Job Description

JOB OVERVIEW The ideal candidate will be responsible for analyzing and interpreting large data sets related to finance , sales and supply chain operations to optimize business processes, identify opportunities for improvement, and provide strategic insights to support decision-making. The Data Scientist will work closely with cross-functional teams to identify key business questions, design and implement statistical models, and develop innovative data-driven solutions. Key Responsibilities: Analyze and interpret large data sets to identify trends, patterns, and opportunities for improvement Develop statistical models and machine learning algorithms to optimize processes, reduce costs, and improve efficiency Collaborate with cross-functional teams to identify business needs, design experiments, and develop solutions that leverage data analytics and machine learning techniques Create and maintain data pipelines and databases to support ongoing data analysis and modeling activities Communicate findings and insights to key stakeholders in a clear and concise manner Continuously monitor and evaluate model performance to ensure accuracy, efficiency, and effectiveness Requirements: Bachelors or masters degree in data science, Statistics, Mathematics, Computer Science, or a related field 3+ years of experience working in a data science role, preferably in a finance or supply chain environment Strong proficiency in programming languages such as Python or R Proficiency in Azure, Snowflake and Airflow Expertise in statistical modeling, machine learning, and data visualization techniques Experience with database technologies such as SQL and NoSQL Excellent communication skills and ability to work collaboratively in a cross-functional team environment Technical Skills: Proficiency in programming languages: The ideal candidate should have strong programming skills in languages such as Python or R, as these are commonly used for data manipulation, analysis, and modeling. They should be able to write clean, efficient, and scalable code to extract, transform, and load data from various sources, and perform complex data analysis tasks using libraries such as NumPy, Pandas, and SciPy. Statistical modeling and machine learning: The candidate should have expertise in statistical modeling and machine learning techniques, including regression analysis, time series forecasting, clustering, classification, and deep learning. They should be able to apply these techniques to real-world problems. Data visualization: The candidate should be able to create meaningful and insightful visualizations of complex data sets using tools such as Matplotlib, Seaborn, or Tableau. They should be able to communicate complex analytical results and insights to non-technical stakeholders in an easily understandable and visually appealing manner. Database technologies: The candidate should have experience working with database technologies such as SQL and NoSQL to extract and transform data from relational and non-relational databases. They should be familiar with concepts such as normalization, indexing, and query optimization, and be able to design and optimize data schemas for efficient data processing and analysis.