Data Scientist

Sobre a vaga

The Data Science department generates value to Riskified by developing algorithms and analytical production-grade solutions. As a Data Scientist, you will assume the classic data-science role of an end-to-end project development and implementation practitioner. You will build and enhance products that matter, leveraging advanced techniques and algorithms to provide maximum value from data.

O que vais fazer

• Data Exploration and Preprocessing: Collect, clean, and transform large, complex data sets from various sources to ensure data quality and integrity for analysis
• Statistical Analysis and Modeling: Apply statistical methods and mathematical models to identify patterns, trends, and relationships in data sets, and develop predictive models
• Machine Learning: Develop and implement machine learning algorithms, such as classification, regression, clustering, and deep learning, to solve business problems and improve processes
• Feature Engineering: Extract relevant features from structured and unstructured data sources, and design and engineer new features to enhance model performance
• Model Development and Evaluation: Build, train, and optimize machine learning models using state-of-the-art techniques, and evaluate model performance using appropriate metrics
• Data Visualization: Present complex analysis results in a clear and concise manner using data visualization techniques, and communicate insights to stakeholders effectively
• Collaborative Problem-Solving: Collaborate with cross-functional teams, including product managers, data engineers, software developers, and business stakeholders to identify data-driven solutions and implement them in production environments
• Research and Innovation: Stay up to date with the latest advancements in data science, machine learning, and related fields, and proactively explore new approaches to enhance the company's analytical capabilities

O que é pedido

• B.Sc (M.Sc is a plus) in Computer Science, Mathematics, Statistics, or a related field
• 3+ years of proven experience designing and implementing machine learning algorithms and successfully deploying them to production
• Strong understanding and practical experience with various machine learning algorithms
• Proficiency in Python, Experience with SQL and data manipulation tools (e.g., Pandas, NumPy) to extract, clean, and transform data for analysis
• Solid foundation in statistical concepts and techniques, including hypothesis testing, regression analysis, time series analysis, and experimental design
• Strong analytical and critical thinking skills to approach business problems, formulate hypotheses, and translate them into actionable solutions
• Proficiency in data visualization libraries, to create meaningful visual representations of complex data
• Excellent written and verbal communication skills to present complex findings and technical concepts to both technical and non-technical stakeholders
• Demonstrated ability to work effectively in cross-functional teams, collaborate with colleagues, and contribute to a positive work environment
• This is a hybrid position based in Lisbon, working 2-3 days from the office weekly

Valorizado

• M.Sc is a plus
• Experience in the fraud domain
• Experience with Airflow, CircleCI, PySpark, Docker and K8S

O que é oferecido

• The base salary range for this position is €60,000 - €70,000 gross annually for candidates based in Lisbon
• All full-time regular employees receive a bonus target and are eligible to receive stock-based awards
• Hybrid mode of work
• Flexible schedule
• Healthcare benefits
• Fully-stocked kitchens
• Benefits package per month-per your choice, e.g., work-from-home equipment, gym membership, wellbeing activities, and more
• Wellness program
• Celebrations and activities
• Team events
• Happy hours
• Awesome Riskified gifts and swags
• Volunteer programs
• Personal development
• Global onboarding
• Role-based technical skills training
• Full access to Udemy

Resumo organizado por IA a partir do anúncio original. Confirma os detalhes no site da empresa.