Senior Data Scientist - New York (Hybrid) - Competitive Salary + Competitive Package + Opportunity t
Orbis Group
Job Description
Senior Data Scientist – New York (Hybrid) – Competitive Salary + Competitive Package + Opportunity to with a Series C Fintech Unicorn!
This well-established FinTech are seeking an experienced Senior Data Scientist to come help build real-time machine learning systems at scale.
This is an excellent opportunity for an experienced Senior Data Scientist to take the next step into a challenging position with a company offering significant growth potential.
About the Company:
Founded about a decade ago, they are now one the most well-known companies in their space.
They are valued at $1.5bn having recently achieved unicorn status.
They are consistently recognized as one of the best FinTech’s and start-ups to work for.
Everything they do is entrenched in achieving engineering excellence.
Their culture is not corporate, and they like to trust their employees to take on a lot of responsibility and have input into the shape of growth of the organisation.
About the Senior Data Scientist Vacancy:
You will work on progressing our core Machine Learning models whilst also partnering directly with customers to drive relevant outcomes.
What You Will Do:
• Participate in the creation, development, and assessment of machine learning models
• Create performance reports, metrics, and testing strategies, and convert results into practical suggestions.
• To guarantee that models maintain accuracy and dependability at scale, support production machine learning activities, such as feature generation, model training, and monitoring.
• To promote shared learning and ongoing development, record findings and share insights with internal teams.
• To guarantee openness and compliance, keep model documentation current and assist model governance procedures.
• Keep up with applicable machine learning industry trends.
Who You Are:
• Constantly considering the final answer
• Capable of explaining complex ideas to a non-technical audience
• Capable of establishing solid cross-functional connections
• Inherently interested and skilled at posing challenging queries
• A strong grasp of fundamental machine learning concepts, including feature engineering, supervised learning, and model evaluation
• A cooperative individual. You think that when the proper individuals collaborate, great things can happen.
• Quick to pick things up
• Be modest. Errors occur, and accepting responsibility for them enables us to grow and advance swiftly.
Ideal Requirements for the Senior Data Scientist Vacancy:
• Over 8 years of experience as an individual contributor in Applied Fraud Research, Data Science, or Machine Learning, demonstrating a successful history in a “Solutions” or client-oriented role
• Proficient in handling highly imbalanced datasets and developing production-level Machine Learning models, particularly focused on tree-based models
• Highly skilled in scripting languages such as Python and in querying languages such as SQL
• Demonstrated capacity to manage and analyze large-scale data (handling billions of records)
• Background in creating metrics and dashboards
• Capable of conveying their results clearly to both technical and nontechnical individuals
• A person who represents their values: take risks, be resourceful, work together, and appreciate our diversity
• You possess experience in a role that requires strong analytical skills within dynamic settings
• Should reside in the Greater New York City area
Nice to Have:
• Prior background in identifying financial fraud
• Postgraduate qualification (Masters or PhD) in a quantitative discipline
• Proficient in overseeing the complete lifecycle of a technical pilot or Proof of Concept
• Familiarity with business intelligence tools such as Looker
• Proficiency in working with graph structure modelling
Apply to the Role:
Roles like these are snapped up very quickly, so act now if you do not want to miss out! Reply to this advert or email your CV to View email address on ziprecruiter.com
$400k
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