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RemoteHunter
RemoteHunter

Data Scientist, Innovation Lab - Remote

Location

Remote restrictions apply
See all remote locations

Salary Estimate

N/AIconOpenNewWindows

Seniority

N/A

Tech stacks

Machine learning
Data
Algorithm
+16

Permanent role
7 days ago
Apply now

1. About Our Client:

The organization is a global data and technology company serving diverse markets including financial services, healthcare, automotive, agribusiness, and insurance. It addresses challenges by combining data, analytics, and software to redefine lending, prevent fraud, simplify healthcare, create marketing solutions, and provide deep market insights. With a workforce of 22,500 across 32 countries and headquarters in Dublin, Ireland, the company invests in advanced technologies and talent to unlock the power of data and support millions in achieving financial goals.

2. About the Opportunity:

The Data Scientist, Innovation Lab role focuses on developing analytical solutions and product prototypes by leveraging predictive modeling, machine learning, and deep learning. This position contributes to evaluating data assets and applying advanced techniques to extract insights from diverse data sources. Reporting to the VP of Data Science, the role is central to enhancing data-driven decision making and fostering innovative data applications within the organization.

3. Responsibilities:

• Develop advanced machine learning solutions for structured and unstructured data

• Select and apply appropriate machine learning, deep learning, and processing techniques

• Design efficient data structures and storage solutions for data manipulation and retrieval

• Innovate with tools for data processing and information retrieval

• Analyze large datasets to identify patterns and insights

• Create algorithms to solve complex problems

• Validate model performance and analyze return on investment and benefits

• Document and present model processes, findings, and performance metrics

4. Requirements:

• Master’s degree or higher in Machine Learning, Data Science, AI, Computer Science, or related quantitative field

• Minimum one year experience in AI, data science, or predictive modeling

• Proficiency in programming, particularly Python

• Experience with deep learning models (CNN, RNN, LSTM, attention models) and machine learning methods (SVM, GLM, boosting, random forest)

• Familiarity with open-source deep learning and machine learning tools such as PyTorch, Keras, TensorFlow, scikit-learn, pandas

• Experience with large-scale data analysis using Spark (PySpark)

• Knowledge of LLMs and tools in the Generative AI domain

• Experience developing advanced language models and applying Generative AI tools

• Experience with Hadoop and NoSQL technologies (MapReduce, Hive, HBase, MongoDB, Cassandra)

• Ability to modify and apply advanced algorithms to practical problems

5. Pay Range and Compensation Package:

• Salary range: $133,109 to $239,596

• Variable pay opportunity available

• Individual pay is determined by work location, skills, experience, and education

6. Benefits & Perks:

• Competitive compensation and bonus plan

• Medical, dental, vision coverage, and matching 401K

• Flexible work environment including remote, hybrid, or in-office options

• Flexible time off including volunteer time, vacation, sick leave, and 12 paid holidays

Equal Opportunity Statement: Our client is an equal opportunity employer. They celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, or national origin.

Note:

RemoteHunter is not the Employer of Record (EOR) for this role. Our purpose in this opportunity is to connect exceptional candidates with leading employers. We help job seekers worldwide discover roles that match their goals and guide them to complete their full application directly through the hiring company’s career page or ATS.

About RemoteHunter

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