Data Scientist

The Transformation Group; New York (NY); Hybrid; USD 90,000 - 130,000; Full time

Job summary

The Transformation Group in New York is seeking a hands-on ML/Data Science Engineer to leverage data management and enhance customer experience. This role will work with our client’s Innovation team to develop next-generation digital platforms and experiences.

Responsibilities include gathering and analyzing data, building scalable ML pipelines, deploying models, and collaborating with product, sales, and other stakeholders to drive measurable business impact.

Qualifications

• Bachelor’s degree in a computational science with over 2 years of postgraduate work in Machine Learning/Statistics/Data Science.
• 2+ years hands-on data cleaning and analysis of large datasets.
• 3+ years with programming languages such as Python, R, Java, C/C++; Linux/Unix environments.
• Experience with large-scale databases and familiarity with AWS products.

Responsibilities

• Gather and analyze data, perform POC analysis from a statistical perspective, identifying key factors, building prototypes and reporting findings to product owners at clients in the US.
• Formulating machine learning/statistical/optimization approaches according to business metrics, designing features from rich data sources, training, evaluating and deploying models.
• Implementing statistical testing to measure the success of projects and business impact.
• Researching forefront techniques, proposing and initiating innovative data science efforts to drive management efficiency and member experience.
• Designing and building scalable data science pipelines, creating high-performance APIs within engineering according to business requirements, maintaining data science pipelines.
• Constructing APIs for data gathering, exploring and performing QA for third party data.
• Learning and sharing knowledge about data science, proposing the capability of data science to business team.

Skills

Machine Learning

Statistics

Data Science

Python

R

Java

C/C++

Linux/Unix

AWS

Data Pipelines

Stakeholder collaboration

Communication

Education

Bachelor's degree in computational science

Master's degree preferred

Tools

AWS

Job description

The ideal candidate should possess hands-on engineering experience using machine learning and statistical tools to leverage data management and customer experience. This role will also be involved with our client’s Innovation team which aims at building up next generation digital platform to bring completely new experiences to their customers.

• Excellent communication skills and ability to communicate with our clients in the US.
• Bachelor’s degree in a computational science with over 2 years of postgraduate work experience in Machine Learning/Statistics/Data Science. Master’s degree is preferred.
• Experience with traditional as well as modern machine learning/statistical techniques, including Regression, Classification, Regularization, Ensemble Methods, Deep Neural Network, Causal Inference and Hypothesis Testing.
• 3+ years of experience with programming languages, such as Python, R, Java, C/C++; familiarity with Linux/Unix/Shell environments, Python preferred.
• 3+ years of experience in working with large-scale database, experience with AWS products is preferred.
• 2+ years hands-on skills in sourcing, cleaning, manipulating and analyzing large volumes of data.
• 2+ years of experience in building and maintaining large scale data/machine learning pipelines in online advertising, recommender system, ecommerce or relevant areas.
• Experience leading machine learning/data science projects.
• Experience working with product, sales and other key stakeholders to drive business.

Responsibilities

• Gathering and analyzing data, performing POC analysis from a statistical perspective, identifying key factors, building prototypes and reporting findings to product owners at clients in the US.
• Formulating machine learning/statistical/optimization approaches according to business metrics, designing features from rich data sources, training, evaluating and deploying models.
• Implementing statistical testing to measure the success of projects and business impact.
• Researching forefront techniques, proposing and initiating innovative data science efforts to drive management efficiency and member experience.
• Designing and building up scalable data science pipelines, creating high-performance APIs within engineering according to business requirements, maintaining data science pipelines.
• Constructing APIs for data gathering, exploring and performing QA for third party data.
• Learning and sharing knowledge about data science, proposing the capability of data science to business team.