Data Scientist

Overview: We are seeking a highly motivated and skilled Data Scientist to join our team in the retail industry. The ideal candidate has at least 2 years of experience in data science, with expertise in data analysis, predictive modeling, and machine learning. Exposure to MLOps, feature engineering, and data engineering workflows will be considered a plus. Responsibilities Data Analysis: Collect, preprocess, and analyze large datasets to identify trends and actionable insights for retail business challenges. Model Development: Design, train, and deploy machine learning models for tasks such as demand forecasting, customer behavior analysis, and inventory optimization. Collaboration: Partner with cross-functional teams, including data engineers and business stakeholders, to translate requirements into data-driven solutions. Visualization and Communication: Present insights and findings through visualizations and dashboards to inform decision-making. Innovation: Stay updated on the latest tools and techniques in data science and retail analytics. Optional Responsibilities (if experienced): Feature Engineering: Engineer and optimize features to improve machine learning model performance. Automate feature extraction pipelines for scalable workflows. MLOps: Contribute to the deployment, monitoring, and retraining of machine learning models in production environments. Data Engineering: Assist in designing and maintaining data pipelines and ensuring data quality. Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field. Experience: At least 2 years of experience in data science or a related field. Technical Skills: Proficiency in Python for data analysis and machine learning. Strong SQL skills for managing and querying large datasets. Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch). Knowledge of data visualization tools (e.g., Tableau, Power BI, matplotlib). Soft Skills: Strong problem-solving, communication, and teamwork abilities. Preferred (Optional) Qualifications: Exposure to MLOps tools (e.g., MLflow, Kubeflow, AWS SageMaker). Familiarity with data engineering tools (e.g., Apache Spark, Kafka, Airflow). Experience in building real-time analytics or personalization systems. Clear focus. Diverse Workplace (Our members are from around the world!) Non-hierarchical and agile environment Growth opportunity and career path