Data Scientist

We are seeking a skilled Machine Learning Engineer , Data Scientist , or Data Analyst to design, develop, and deploy machine learning models, conduct deep data analysis, and generate actionable insights. The ideal candidate will have experience in data preprocessing, feature engineering, model development, and performance optimization, working with large datasets and leveraging advanced machine learning frameworks. Required Skills: Programming Languages : Strong proficiency in Python , with experience in SQL . Machine Learning : Hands-on experience with Scikit-learn , TensorFlow , Keras , PyTorch , or similar ML libraries. Data Analysis : Strong skills in Pandas , NumPy , and Matplotlib for data manipulation and analysis. Statistical Analysis : Experience applying statistical methods to data, including hypothesis testing and regression analysis. Cloud Platforms : Familiarity with AWS , Azure , or Google Cloud for deploying models and using cloud-native data services (e.g., AWS Sagemaker , Azure ML ). Data Visualization : Experience using Tableau , Power BI , Matplotlib , Seaborn , or Plotly for creating visualizations. SQL & Databases : Proficiency in SQL for querying relational databases and working with NoSQL databases (e.g., MongoDB , BigQuery ). Version Control : Experience using Git for version control. Desirable Skills: Big Data Technologies : Familiarity with tools like Apache Hadoop , Spark , Dask , or Google BigQuery for processing large datasets. Deep Learning : Experience with deep learning frameworks such as TensorFlow , PyTorch , or MXNet . NLP & Computer Vision : Experience with natural language processing (NLP) using spaCy , NLTK , or transformers , and computer vision using OpenCV or TensorFlow . MLOps : Familiarity with MLOps tools like Kubeflow , MLflow , or DVC for managing model workflows. Data Engineering : Experience with ETL tools like Apache Airflow , Talend , AWS Glue , or Google Dataflow for data pipeline automation. Tools & Technologies: Machine Learning : Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost. Data Analysis : Pandas, NumPy, Matplotlib, Seaborn, Plotly. Cloud Platforms : AWS, Google Cloud, Azure. Databases : MySQL, PostgreSQL, MongoDB, BigQuery, Snowflake. Data Visualization : Tableau, Power BI, Matplotlib, Seaborn, Plotly. Version Control : Git.

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