Data Science Leader
4 weeks ago
Job Overview
\Data Scientists at ZipRecruiter play a critical role in driving business innovation through machine learning. As a Data Scientist, you will be responsible for the entire Model Development Life Cycle (MDLC), from problem definition to model deployment and monitoring.
\Key Responsibilities:
\- \
- Collaborate with business stakeholders to define and structure data-driven problems, translating business objectives into machine learning tasks such as classification, regression, or clustering.\
- Gather, clean, and preprocess data from multiple sources, handle missing data, outliers, and apply normalization techniques.\
- 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, applying techniques such as feature extraction, selection, and transformation.\
- Select appropriate machine learning models based on the problem at hand and train them using tools like Scikit-learn, TensorFlow, or PyTorch. Evaluate model performance using relevant metrics and optimize hyperparameters for robustness.\
- Deploy models in a production environment using tools like Flask, FastAPI, Docker, and Kubernetes, ensuring scalability and integration with existing systems.\
- Monitor model performance post-deployment, address model drift, and retrain models as needed to maintain continuous accuracy and relevance.\
- Provide clear and actionable insights through model interpretation techniques and present results to both technical and non-technical stakeholders.\\-
Requirements:
\To succeed in this role, you should have a PhD degree in Computer Science, Data Science, Statistics, Engineering, or a related field, along with 3+ years of experience in machine learning, statistical modeling, and data science.
\You should be proficient in Python, SQL, and experienced with libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, and Keras. Additionally, you should have hands-on experience with model deployment tools like Flask, Docker, Kubernetes, and cloud platforms like AWS, Azure, or Google Cloud.
\A strong understanding of data preprocessing techniques, feature engineering, and exploratory data analysis is essential, as well as experience with hyperparameter tuning techniques and model monitoring tools like MLflow, Prometheus, or Grafana.
\Familiarity with deep learning models, NLP, time-series analysis, big data tools, and experience in sectors like healthcare, finance, or e-commerce are desirable but not required.
\Estimated Salary: $125,000 - $160,000 per year
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