The role of Senior Data Science is one giant leap from technology to leadership. As project leaders, they communicate data-driven insights with business stakeholders to create a roadmap for revenue growth.
Hiring a talented candidate is dependent on how well you outline the senior data scientist job description. That's why we are here to give out the best of the hiring resources for your perusal.
Senior Data Scientist Job Description
We are hiring a Senior Data Scientist who has the best research, technical and functional expertise in neural networks optimization (quantization, fusing, folding)
Candidates must be adept in multiple deep learning models like CNNs, LSTM, ResNet, YOLO, and MobileNet.
Certifications from SAS, DASCA, Microsoft, and Azure are a bonus!
What are we looking for?
- Advanced degrees in computer science, statistics, applied sciences, or econometrics (nice to have)
- Strong hold on technical development phases viz., data engineering, analytics/modeling, and visualization/user interface
- Proficiency in converting large data sets for a variety of data mining/data analysis methods.
- Prior experience in data science/machine learning models and algorithms
- Expertise in Python, Machine Learning, SQL, Apache Hadoop, and predictive models
What a day in the life of a Senior Data Scientist will look like?
- Work with Product Engineering and Customer Support to identify problems in different areas and determine where data mining/machine learning/statistics can help
- Deploy production-scale solutions using the Hadoop Ecosystem, transforming statistical and machine learning models from single-node architecture to parallel processing grid technology
- Apply advanced machine learning algorithms, statistical methods, and predictive modeling techniques on large and varied data sets that include application log files, another online application telemetry, structured and unstructured data sources
- Collaborate daily with other analytics team members implementing data analysis and machine learning techniques
- Voluntarily direct data science initiatives to uncover business value using statistical, machine learning, and analytics techniques
- Design and develop research methods that address questions related to predicting educational outcomes, designing impactful software, and other strategic issues
- Analyze research results, derive actionable insights, and present findings to product management and development personnel
- Lead the development of business requirements and functional algorithms that can be used in real-time production environments
- Provide ongoing tracking and monitoring of the performance of algorithms and statistical models, troubleshoot and implements enhancements and fixes to these systems as needed
- Assess data sources for integrity and validity compared to intended educational processes
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