ASCENDING is hiring a
Data Scientist

Summary

Join our team as a Data Scientist to manage the complete Model Development Life Cycle, collaborate with cross-functional teams, and deliver machine learning models that support business objectives. The ideal candidate should have a strong background in data analysis, feature engineering, and model selection, along with a deep understanding of model deployment and ongoing model maintenance.

Requirements

• PhD degree in Computer Science, Data Science, Statistics, Engineering, or a related field
• 3+ years of experience in machine learning, statistical modeling, and data science
• Proficiency in Python, SQL, and experience with libraries such as Pandas. , NumPy. , Scikit-learn. , TensorFlow. , and Keras
• Hands-on experience with model deployment tools such as Flask. , Docker. , Kubernetes. , and cloud platforms like AWS. , Azure. , or Google Cloud
• Strong knowledge of data preprocessing techniques, feature engineering, and exploratory data analysis
• Experience with hyperparameter tuning techniques (e.g., Grid Search, Bayesian Optimization)
• Familiarity with model monitoring tools such as MLflow. , Prometheus. , or Grafana
• Excellent communication skills, with the ability to translate technical results into actionable insights for stakeholders
• Strong problem-solving skills and the ability to work on complex, data-driven projects

Responsibilities

• Collaborate with business stakeholders to define and structure data-driven problems
• Gather, clean, and preprocess data from multiple sources (e.g., databases, APIs, publicly available datasets)
• Use statistical analysis and data visualization techniques to identify key patterns, trends, and correlations in the data
• Create, extract, and transform features to improve model performance
• Select the appropriate machine learning models based on the problem at hand (e.g., supervised learning, unsupervised learning, deep learning)
• Train models using tools like Scikit-learn. , TensorFlow. , or PyTorch.. Evaluate model performance using relevant metrics (e.g., RMSE, accuracy, F1-score, ROC-AUC) and optimize hyperparameters to ensure robustness
• Deploy models in a production environment using tools like Flask. , FastAPI. , Docker. , and Kubernetes.. Ensure scalability and integration with existing systems
• Monitor model performance post-deployment, address model drift, and retrain models as needed
• Provide clear and actionable insights through model interpretation techniques such as feature importance and SHAP values

Preferred Qualifications

• Experience with deep learning models (e.g., CNNs, RNNs, LSTMs)
• Familiarity with NLP and time-series analysis
• Knowledge of big data tools like Spark. or Hadoop
• Experience in sectors such as healthcare, finance, or e-commerce