IT|Data Analysis - Data Scientist I
E-Solutions, Santa Clara, CA, United States
Client Job Description: 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.
Job Description: Expectations from this role:
1. Work with stakeholders creating quick prototypes of the solution to define analytics roadmap.
2. Work with business to understand business domain and convert business problems into analytics problems
3. Create or use existing frameworks to test and validate new models
4. Explain models and put results in easy to interpret manner such that non-analytic person can understand
5. Turn data into information that can improve current workflow or processes that will help make better business decisions. Generate business insights to help clients with decision making.
6. Adhere to best practices of codingTypical performance measures:
1. Schedule Adherence
2. Quality of code and other deliverables
3. Number of reusable components developed
4. New analytics solutions that got approved for further development Performance Areas:
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:
Visualize and present 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:
Creates efficient and reusable code meant for the improvement, manipulation, and analysis of data.
Version Control:
Manages project codebase through a version control tool e. g. 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 and client universities
Status Reporting:
Report status of tasks assigned
Comply with project related reporting standards and 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 stakeholders.
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:
1. Excellent pattern recognition and predictive modelling skills
2. Extensive background in data mining and statistical analysis
3. Expertise in machine learning techniques and creating algorithms.
4. Ability to work with structured, semi-structured and unstructured datasets.
5. Ability to learn and implement new Data Science algorithms in a fast turnaround time
6. Analytical Skills: Ability to work with large amounts of data: facts, figures, and number crunching.
7. Communication Skills: Communicate effectively with a diverse population at various organization levels with the right level of detail.
8. Critical Thinking: Data Analysts must look at the numbers, trends, and data and come with new conclusions based on the findings.
9. Strong meeting facilitation skills as well as presentation skills.
10. Attention to Detail: Making sure to be vigilant in the analysis to come to correct conclusions.
11. Mathematical Skills to estimate numerical data.
12. Work in a team environment
13. Proactively ask for and offer help
14. Break big problems into small components and define the solution architecture.
15. Explains complex models (like CNN, RNN, XGBoost etc.) in a manner which is easy to understand for a nontechnical stakeholder.
16. Good understanding of known data science tools and technology like H20, Neo4j, PySpark, etc.Knowledge Examples:
1. Programming languages - Java/ Python/ R.
2. Web Services - Redshift, S3, Spark, DigitalOcean, etc.
3. Statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
4. Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
5. Computing Tools - Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
6. Database languages such as SQL, NoSQL
7. Analytical tools and languages such as SAS & Mahout.
8. Practical experience with ETL, data processing, etc.
9. Proficiency in MATLAB.
10. Data visualization software such as Tableau or Qlik.
11. Proficient in mathematics and calculations.
12. Spreadsheet tools such as Microsoft Excel or Google Sheets
13. DBMS
14. Operating Systems and software platforms
15. Knowledge about customer domain and sub domain where problem is solved
16. Proficient on at least 1 version control tool like git, bitbucket
17. Have experience working with project management tool like Jira
18. Data Science based high performance application development knowledge is required.
19. NLP and Computer Vision tools and libraries - OpenCV, spaCY, Transformers, Attention models etc.
Additional Sills: Image Filtering, Binary Morphology, Perspective / Affine transformation, Edge Detection, and Tracking, Machine Learning, Regression, Binary, Pca Skills:
Category
Name
Required
Importance
Experience
No items to display.
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.
Job Description: Expectations from this role:
1. Work with stakeholders creating quick prototypes of the solution to define analytics roadmap.
2. Work with business to understand business domain and convert business problems into analytics problems
3. Create or use existing frameworks to test and validate new models
4. Explain models and put results in easy to interpret manner such that non-analytic person can understand
5. Turn data into information that can improve current workflow or processes that will help make better business decisions. Generate business insights to help clients with decision making.
6. Adhere to best practices of codingTypical performance measures:
1. Schedule Adherence
2. Quality of code and other deliverables
3. Number of reusable components developed
4. New analytics solutions that got approved for further development Performance Areas:
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:
Visualize and present 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:
Creates efficient and reusable code meant for the improvement, manipulation, and analysis of data.
Version Control:
Manages project codebase through a version control tool e. g. 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 and client universities
Status Reporting:
Report status of tasks assigned
Comply with project related reporting standards and 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 stakeholders.
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:
1. Excellent pattern recognition and predictive modelling skills
2. Extensive background in data mining and statistical analysis
3. Expertise in machine learning techniques and creating algorithms.
4. Ability to work with structured, semi-structured and unstructured datasets.
5. Ability to learn and implement new Data Science algorithms in a fast turnaround time
6. Analytical Skills: Ability to work with large amounts of data: facts, figures, and number crunching.
7. Communication Skills: Communicate effectively with a diverse population at various organization levels with the right level of detail.
8. Critical Thinking: Data Analysts must look at the numbers, trends, and data and come with new conclusions based on the findings.
9. Strong meeting facilitation skills as well as presentation skills.
10. Attention to Detail: Making sure to be vigilant in the analysis to come to correct conclusions.
11. Mathematical Skills to estimate numerical data.
12. Work in a team environment
13. Proactively ask for and offer help
14. Break big problems into small components and define the solution architecture.
15. Explains complex models (like CNN, RNN, XGBoost etc.) in a manner which is easy to understand for a nontechnical stakeholder.
16. Good understanding of known data science tools and technology like H20, Neo4j, PySpark, etc.Knowledge Examples:
1. Programming languages - Java/ Python/ R.
2. Web Services - Redshift, S3, Spark, DigitalOcean, etc.
3. Statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
4. Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
5. Computing Tools - Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
6. Database languages such as SQL, NoSQL
7. Analytical tools and languages such as SAS & Mahout.
8. Practical experience with ETL, data processing, etc.
9. Proficiency in MATLAB.
10. Data visualization software such as Tableau or Qlik.
11. Proficient in mathematics and calculations.
12. Spreadsheet tools such as Microsoft Excel or Google Sheets
13. DBMS
14. Operating Systems and software platforms
15. Knowledge about customer domain and sub domain where problem is solved
16. Proficient on at least 1 version control tool like git, bitbucket
17. Have experience working with project management tool like Jira
18. Data Science based high performance application development knowledge is required.
19. NLP and Computer Vision tools and libraries - OpenCV, spaCY, Transformers, Attention models etc.
Additional Sills: Image Filtering, Binary Morphology, Perspective / Affine transformation, Edge Detection, and Tracking, Machine Learning, Regression, Binary, Pca Skills:
Category
Name
Required
Importance
Experience
No items to display.