Data Scientist (PhD) - ASCENDING

Job Overview:
As a Data Scientist, you will be responsible for managing the complete Model Development Life Cycle (MDLC), from problem definition to model deployment and monitoring. You will work closely with cross-functional teams to deliver machine learning models that support business objectives and drive innovation. 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.

Key Responsibilities:

• Problem Definition: Collaborate with business stakeholders to define and structure data-driven problems. Translate business objectives into machine learning tasks (e.g., classification, regression, clustering).
• Data Collection & Preprocessing: Gather, clean, and preprocess data from multiple sources (e.g., databases, APIs, publicly available datasets). Handle missing data, outliers, and apply normalization techniques.
• Exploratory Data Analysis (EDA): Use statistical analysis and data visualization techniques to identify key patterns, trends, and correlations in the data.
• Feature Engineering: Create, extract, and transform features to improve model performance. Apply techniques such as feature extraction, selection, and transformation.
• Model Selection & Training: 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.
• Model Monitoring & Maintenance: Monitor model performance post-deployment, address model drift, and retrain models as needed. Ensure continuous accuracy and relevance of models in real-world scenarios.
• Model Interpretation & Communication: Provide clear and actionable insights through model interpretation techniques such as feature importance and SHAP values. Present results to both technical and non-technical stakeholders.

Qualifications:

• 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.

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.

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