Data Scientist — Hybrid ML & Analytics Impact

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Lisbon (IN)

Hybrid

USD 68,000 - 79,000

Full time

14 days+

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Benefits offered by this job

Hybrid mode of work

Flexible schedule

Healthcare benefits

Fully-stocked kitchens

Benefits package per month—per your 0h

Job summary

Riskified is seeking a Data Scientist to drive end-to-end data-driven solutions. You will develop algorithms, apply advanced analytics, and deploy models to production, tackling fraud, risk, and customer insights across ecommerce contexts.

You will collaborate with product managers, data engineers, and software developers to translate complex data into actionable business results, while staying current with ML innovations and best practices. Hybrid work options available in Lisbon.

Qualifications

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

Responsibilities

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