- Providing solutions for the deployment, execution, validation, monitoring, and improvement of data science solutions
- Creating Scalable Machine Learning systems that are highly performant
- Building reusable production data pipelines for implemented machine learning models
- Writing production-quality code and libraries that can be packaged as containers, installed and deployed
- Support ML projects from strategy through implementation and on-going improvements.
- Perform data collection, analysis, validation, cleansing, developing software in support of multiple
- Designs, codes, tests, debugs, and documents ML code - models, ETL processes, SQL queries, and
- Extracts and analyzes data from various structured and unstructured sources, including databases,
- Responds to data inquiries from various groups within client's organization.
- Requires experience with relational databases, document databases (NOSQL) and knowledge of
- Responsible for other duties/projects as assigned by business management / leadership.
- 7 plus years of experience in statistical modeling, data mining, analytics techniques, machine
- 5 plus years of applied experience in building / deploying Machine Learning solutions using
- 5 plus years of hands-on experience with Python and/or R programming and statistical
- 3 plus years of experience in building use cases / solutions especially around AI/
- Expertise with SQL, noSQL, Python, R, Javascript programming languages and big data
- Experience developing experimental and analytic plans for data modeling processes, using
- Understanding of relevant statistical measures such as confidence intervals, significance of error
- Expertise with scaling pilot machine learning solutions to a large scale production environment using databricks
- Expertise with visualization tools such as PowerBI, D3JS etc.
- Excellent written and verbal communication skills.
- Bachelor or Masters degree in highly quantitative field (computer science, or electrical
- Proficient in machine learning data workflows, data collection methodologies, and data analysis.
- Experience with architecting, designing, developing software solution in Azure and on-prem
- Certifications AI / ML and Azure Cloud platforms
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Machine Learning Engineer - Seattle, United States - Tiger Analytics
Description
Job Description
Job DescriptionTiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
We are looking for a motivated and passionate Machine Learning Engineers for our team.
As part of this job, you will be responsible for:
Requirements
Essential Functions
machine learning workflows, integrating / deployment of code in a large-scale production
environments and reporting.
stored procedures.
files, data lakes and external APIs/websites.
query tools and/or statistical software.
Qualifications
Minimum Required
learning software development and reporting
various supervised/unsupervised ML algorithms such as Linear/Logistic Regression, Support
Vector Machines, (Deep) Neural Networks, Random Forest, etc., and key parameters that affect
their performance.
packages, and ML libraries such as scikit-learn, TensorFlow, PyTorch, etc.
based on Cloud infrastructure and services such as Azure,GCP,AWS cloud platforms and Onpremise
environments
environments (such as Splunk, Hadoop, Spark, Flink, Stream Analytics, Kafka, Docker,
Kubernetes etc.)
strong baselines, and determining cause and effect relations.
measurements, development and evaluation data sets, etc. in data analysis projects.
Desired
engineering, mathematics, statistics) or equivalent domain specific experience in lieu of a
degree.
environments.
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.