Data Scientist (PhD)

Job Description Location: 100% Remote within the United States

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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