Data Scientist- AI

Data Scientist- AI at Radancy

Radancy Programmatic is actively seeking a Data Scientist- AI to bolster its endeavors in developing a leading performance media platform tailored for recruitment. The Radancy Data Science team is distinguished by its profound expertise in optimization, regression, classification, and Natural Language Processing (NLP) principles. This expertise is leveraged to craft world-class machine learning strategies for performance advertising, consistently yielding the most critical outcomes for our clients: securing the best employees.

Role and Responsibilities

• Building sophisticated machine learning models designed to optimize campaign performance on an extensive scale.
• Identifying, creating, and meticulously transforming informative features derived from both historical and real-time data streams.
• Collaborating closely with the Data Science Lead and Product Lead to ensure the implementation of a coherent and consistent technical direction.
• Contributing to the definition of robust data collection and modeling frameworks.
• Coordinating and working in tight conjunction with engineering teams to seamlessly transition models and algorithms into production environments.
• Engaging collaboratively with other Data Science teams on cross-product initiatives to foster synergy and comprehensive solutions.

Requirements and Background

• Possession of 2+ years of experience in Data Science or an equivalent professional background.
• A deep and comprehensive understanding of statistical and machine learning models, including regression, decision trees, and neural networks, as well as various clustering and classification techniques.
• Proven experience with keyword embedding techniques.
• Practical experience in building models and successfully deploying them into production.
• Demonstrated expertise in tackling optimization problems and effectively solving them at scale.
• Proficiency in statistical programming languages such as R or Python.
• Familiarity with relational databases like Postgres and Amazon Redshift, alongside the ability to access data proficiently via SQL.
• A degree in a relevant technical field, such as computer science, mathematics, economics, or engineering.
• Graduate-level work in machine learning is a significant advantage, or equivalent relevant work experience.
• Experience within “Ad Tech” data science principles is considered a substantial plus.

Personality Traits

• Quick thinking and capable of acting autonomously with minimal or no supervision.
• Self-driven, independent, creative, and possessing a strong eagerness to continuously acquire new skills.
• Adept at working effectively even with incomplete information.
• Excellent communication skills, both written and verbal.
• Experience with and a genuine desire to contribute to an asynchronous, remote, and globally distributed team.

Company Values

At Radancy, our work and culture are guided by core values:

• Innovative Spirit: Leading with innovation, valuing every team voice to shape the future.
• Rewarding Impact: Strengthening organizations through transformative software that connects people to meaningful careers.
• Performance Driven: Using data and insights to maximize customer performance, backed by global expertise.
• Supportive Teams: Fostering collaboration, empowering learning, growth, and positive change.

Benefits

Radancy offers a comprehensive benefits package designed to accelerate careers and enhance well-being, including:

• Comprehensive medical, dental, vision, and life insurance.
• Competitive 401(k) plan with employer matching.
• Unlimited flexible time off (FTO).
• World-class training to stay at the forefront of innovation.

We champion work-life balance and embrace diversity, equity, and inclusion, cultivating a dynamic, supportive culture where every voice contributes to our success. Flexible wellness programs and a mission-driven environment ensure you thrive.

Key skills/competency

• Machine Learning
• Statistical Modeling
• Optimization
• Python
• SQL
• Ad Tech
• NLP
• Data Analysis
• Feature Engineering
• Model Deployment