Data Scientist

Description:

Required Skill Set – Data Scientist, Data Engineer

Programming & Tools

· Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch)Working knowledge of statistical analysis and modelingExperience with Jupyter Notebooks, RStudio, and data visualization toolsFamiliarity with SQL and data querying

Machine Learning & AI

· Solid understanding of machine learning algorithms (supervised & unsupervised)Hands-on experience with:Regression (linear, logistic)Classification (decision trees, random forests, SVM)Clustering (K-means, hierarchical)

· Experience with deep learning frameworks (TensorFlow, PyTorch) is a plus

· Predictive Modeling

· Proven experience in predictive modeling and forecastingAbility to build, validate, and deploy predictive modelsStrong understanding of:

· Feature engineeringModel evaluation techniques (ROC, precision/recall, cross-validation)

· Experience working with real-world datasets to derive actionable insights

· Statistics & Data Analysis

· Strong foundation in statistics and probabilityHypothesis testing, regression analysis, and statistical modelingData cleaning, transformation, and exploratory data analysis (EDA)

· Data & Deployment (Preferred)

· Experience with cloud platforms (AWS, Azure, or GCP)Familiarity with Docker/containers is a plusExposure to MLOps practices (CI/CD for ML models)

· Soft Skills

· Strong analytical and problem-solving skillsAbility to translate business problems into data solutionsEffective communication and storytelling with dataCollaborative mindset with cross-functional teams

· Nice-to-Have

· Experience with big data tools (Spark, Hadoop)Exposure to NLP, computer vision, or time-series forecastingKnowledge of model deployment APIs (Flask, FastAPI)

· As a Data Engineer you’ll be responsible for acquiring, curating, and publishing data for analytical or operational uses. Data should be in a ready-to-use form that creates a single version of the truth across all data consumers, including business users, data scientists, and Technology. Ready-to-use data can be for both real time and batch data processes and may include unstructured data. Successful data engineers have the skills typically required for the full lifecycle software engineering development from translating requirements into design, development, testing, deployment, and production maintenance tasks. You’ll have the opportunity to work with various technologies from big data, relational and SQL databases, unstructured data technology, and programming languages.