Job Description

Tiger 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:

• 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

Essential Functions

• Support ML projects from strategy through implementation and on-going improvements.
• Perform data collection, analysis, validation, cleansing, developing software in support of multiple

machine learning workflows, integrating / deployment of code in a large-scale production

environments and reporting.

• Designs, codes, tests, debugs, and documents ML code - models, ETL processes, SQL queries, and

stored procedures.

• Extracts and analyzes data from various structured and unstructured sources, including databases,

files, data lakes and external APIs/websites.

• Responds to data inquiries from various groups within client’s organization.
• Requires experience with relational databases, document databases (NOSQL) and knowledge of

query tools and/or statistical software.

• Responsible for other duties/projects as assigned by business management / leadership.

Qualifications

Minimum Required

• 7 plus years of experience in statistical modeling, data mining, analytics techniques, machine

learning software development and reporting

• 5 plus years of applied experience in building / deploying Machine Learning solutions using

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.

• 5 plus years of hands-on experience with Python and/or R programming and statistical

packages, and ML libraries such as scikit-learn, TensorFlow, PyTorch, etc.

• 3 plus years of experience in building use cases / solutions especially around AI/

based on Cloud infrastructure and services such as Azure,GCP,AWS cloud platforms and Onpremise

environments

• Expertise with SQL, noSQL, Python, R, Javascript programming languages and big data

environments (such as Splunk, Hadoop, Spark, Flink, Stream Analytics, Kafka, Docker,

Kubernetes etc.)

• Experience developing experimental and analytic plans for data modeling processes, using

strong baselines, and determining cause and effect relations.

• Understanding of relevant statistical measures such as confidence intervals, significance of error

measurements, development and evaluation data sets, etc. in data analysis projects.

• 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.

Desired

• Bachelor or Masters degree in highly quantitative field (computer science, or electrical

engineering, mathematics, statistics) or equivalent domain specific experience in lieu of a

degree.

• Proficient in machine learning data workflows, data collection methodologies, and data analysis.
• Experience with architecting, designing, developing software solution in Azure and on-prem

environments.

• Certifications AI / ML and Azure Cloud platforms

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.

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