Data Scientist

• Collect, clean, and analyze large datasets.
• Build predictive and machine learning models.
• Perform exploratory data analysis (EDA) to identify trends and insights.
• Develop statistical models to solve business problems.
• Create dashboards and visualizations for stakeholders.
• Validate, monitor, and improve model performance.
• Collaborate with data engineers, analysts, and business teams.
• Present findings and recommendations to technical and non-technical audiences.
• Automate data processing and model deployment workflows.
• Stay current with new AI, ML, and data science technologies.

Skills: Python,PyTorch ,SQL, Scala, Java, Data Analysis, Data Visualization, Pandas, NumPy, , Tableau, Power BI,EDA, * Python (Pandas, NumPy, Scikit-learn)

• SQL for data extraction and analysis
• Statistics and Probability
• Machine Learning (Supervised & Unsupervised Learning)
• Data Cleaning and Feature Engineering
• Data Visualization (Tableau, Power BI, Matplotlib, Seaborn)
• Exploratory Data Analysis (EDA)
• Predictive Modeling
• Model Evaluation and Validation
• Deep Learning (TensorFlow or PyTorch) preferred
• Version Control (Git)
• Cloud platforms (AWS, Azure, or Google Cloud)

Preferred Skills

• NLP (Natural Language Processing)
• Large Language Models (LLMs)
• Generative AI
• Apache Spark or Databricks
• MLOps (MLflow, Kubeflow)
• Docker and Kubernetes
• Big Data technologies (Hadoop, Hive)
• A/B Testing and Experimentation
• Time Series Forecasting
• Recommendation Systems