Data Scientist - PhD

September 24

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ASCENDING Inc.

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Software Engineer candidate • aws • devops • cloud engineer • java

11 - 50 employees

Description

• 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. • Work closely with cross-functional teams to deliver machine learning models that support business objectives and drive innovation. • 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 and train models using tools like Scikit-learn, TensorFlow, or PyTorch. • Monitor model performance post-deployment, address model drift, and retrain models as needed. • Provide clear and actionable insights through model interpretation techniques and present results to stakeholders.

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. • 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.