Data Scientist- Computer Vision
Santa Clara, CA (Onsite)
6-12 Months
Role: Data Scientist -- Computer Vision UST Global® team is looking for Data Scientist for their client. The candidate must have the below skillset to be successful in this role. 1. Solid knowledge of various Image Filtering, Binary Morphology, Perspective / Affine transformation, Edge Detection, and Tracking. 2.
Machine Learning: Regression, Unsupervised Learning, PCA. 3. Nice but not necessary to have HDR, Panorama, and deep Learning object detection.
Statistical Techniques: Apply statistical techniques like regression properties of distributions statistical tests etc. to analyse data. Machine Learning Techniques: Apply machine learning techniques like clustering decision tree learning artificial neural networks etc to streamline data analysis. Creating advanced algorithms: Create advanced algorithms and statistics using regression simulation scenario analysis modelling etc. Data Visualization: Visualizing/presenting data for stakeholders using: Periscope Business Objects D3 ggplot etc.
Management and Strategy: Oversees the activities of analyst personnel and ensures the efficient execution of their duties.
Critical business insights: Mines the business's database in search of critical business insights and communicates findings to the relevant departments. Code: Creating efficient and reusable code meant for the improvement manipulation and analysis of data. Version Control: Manages project codebase through a version control tool like git bitbucket etc. Predictive analytics: Seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analysis Prescriptive analytics: Attempts to identify what business action to take Create Reports: Create reports depicting the trends and behaviours from the analysed data Training end users on new reports and dashboards. Set FAST goals and provide feedback to FAST goals of mentees Document: Create documentation for own work as well as perform peer review of documentation of others' work Manage knowledge: Consume and contribute to project related documents share point libraries client universities
Status Reporting: Report status of tasks assigned Comply to project related reporting standards/process Solution Architecture: Create modular architecture such that it can be reused easily across projects. Analysis: Report trends for anomalies outliers trend changes and opportunities from a given data set Data Gathering: Partner with business and gather relevant data Modelling: Data models and experiments for solving business problems Stakeholder management Explain findings to both technical and nontechnical stakeholder. New business development: Create solution architecture and quick prototypes to explain analytics roadmap to a business team. Team Management: Increase team productivity by upskilling them technically.
Skill Examples: Excellent pattern recognition and predictive modelling skills Extensive background in data mining and statistical analysis Expertise in machine learning techniques and creating algorithms. Ability to work with structured semi-structured and unstructured datasets. Ability to learn and implement new Data Science algorithms in a fast turnaround time Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching. Communication Skills: Communicate effectively with diverse people at various organization levels with the right level of detail. Critical Thinking: Data analysts must look at the numbers trends and data and come to new conclusions based on the findings. Strong meeting facilitation skills as well as presentation skills. Attention to Detail: Making sure to be vigilant in the analysis to come to correct conclusions. Mathematical Skills to estimate numerical data. Work in a team environment Ask proactively for help and offer help proactively Break big problem into small components and define the solution archit
1. Solid knowledge of various Image Filtering, Binary Morphology, Perspective / Affine transformation, Edge Detection, and Tracking.
2. Machine Learning: Regression, Unsupervised Learning, PCA.
3. Nice but not necessary to have HDR, Panorama, and deep Learning object detection.
Apply statistical techniques like regression properties of distributions statistical tests etc. to analyse data.
Machine Learning Techniques:
Apply machine learning techniques like clustering decision tree learning artificial neural networks etc to streamline data analysis.
Creating advanced algorithms:
Create advanced algorithms and statistics using regression simulation scenario analysis modelling etc.
Comments for Suppliers:
Looking for Data Scientist with the below.
1. Solid knowledge of various Image Filtering, Binary Morphology, Perspective / Affine transformation, Edge Detection, and Tracking.
2. Machine Learning: Regression, Unsupervised Learning, PCA.
Additional Skills:
Image Filtering, Binary Morphology, Perspective / Affine transformation, Edge Detection, and Tracking, Machine Learning, Regression, Binary, Pca
Warm Regards
Bhaskar kumar | Senior Recruiter
3S Business Corporation
kumar.koppisetti@3sbc.com
16700 HOUSE HAHL RD BLDG 6B, Cypress, TX-77433
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