Nift is disrupting performance marketing, delivering millions of new customers to brands every month. We’re actively looking for a hands-on Senior Data Scientist to focus on building ML models for scalable consumer-based services (or non-consumer services that require highly scalable ML models for predictions and classifications).
As a Senior Data Scientist, you’ll report to the Data Science Manager and work closely with both our Data Science and Engineering teams. You will play a crucial role in developing models for complex parts of our business. You will analyze large datasets, identify patterns, and develop a variety of model types. You’ll also contribute to the improvement and re-design of our current models. You will collaborate closely with cross-functional teams to translate insights into actionable solutions. This is a hands-on position where you will apply your expertise in statistical modeling, machine learning, and data mining techniques.
This role is ideally based in Israel, but strong candidates from the U.S. will also be considered.
Our Mission:
Nift’s mission is to reshape how people discover and try new brands by introducing them to new products and services through thoughtful "thank-you" gifts. Our customer-first approach ensures businesses acquire new customers efficiently while making customers feel valued and rewarded.
We are a data-driven, cash-flow-positive company that has experienced 731% growth over the last three years. Now, we’re scaling to become one of the largest sources for new customer acquisition worldwide. Backed by investors who supported Fitbit, Warby Parker, and Twitter, we are poised for exponential growth and ready to demonstrate impact on a global scale. Read more about our growth here.
What you will do:
- Data Analysis and Exploration: You will need to explore and analyze large volumes of data to gain insights and identify patterns relevant to your modeling objectives. This involves data cleaning, preprocessing, and transforming data into a suitable format for modeling
- Model Development: You will design and develop models using statistical and machine-learning techniques. This includes selecting appropriate algorithms, feature engineering, model training, and evaluation
- Data Preparation: You will be responsible for preparing the data required for modeling, including gathering and integrating data from various sources, ensuring data quality and consistency, and defining appropriate features and variables
- Model Evaluation and Testing: You will assess the performance and accuracy of the models using appropriate evaluation metrics. This includes conducting experiments, cross-validation, and measuring the effectiveness of recommendations
- Optimization and Tuning: You will fine-tune models to optimize their performance, improve accuracy, reduce bias or overfitting, and enhance the efficiency of the algorithms. provide actionable recommendations
- Analyze large datasets to identify patterns, trends, and insights that can be leveraged to improve business performance
- Design, build and evaluate systems to personalize consumer experience and drive customer engagement
- Collaborate with cross-functional teams to understand business requirements and translate them into data-driven solutions
- Conduct rigorous testing and validation of models to ensure their accuracy, robustness, and reliability
- Monitor model performance, identify areas of improvement, and continuously refine models based on new data and evolving business needs
- Stay up-to-date with the latest advancements in data science, machine learning, and recommendation system technologies, and apply them to solve business challenges
What You Need:
- Master's or Ph.D. in a quantitative field such as Computer Science, Statistics, Applied Mathematics, Economics, Physics, or a related discipline
- 5+ years of experience working in a professional setting deploying models
- Strong experience in building and deploying into production predictive models and recommendation systems using statistical modeling, machine learning, and data mining techniques
- Proficiency in Machine Learning: You should have a deep experience with various machine learning techniques, such as regression, classification, clustering, dimensionality reduction, and ensemble methods. Familiarity with popular algorithms like decision trees, random forests, Boosted trees, and regularized regression.
- Experience with match-propensity models, embeddings based models, cold-start handling, calibration/post-processing, integrating models into ad-tech workflows and business logic, measuring model performance within ad-tech systems.
- Strong Background in Statistics and Mathematics: A solid foundation in statistical concepts, linear algebra, calculus, and probability theory is essential for understanding the principles behind machine learning algorithms and recommendation systems
- Proficiency in programming languages such as Python or R, along with experience in data manipulation and analysis using libraries like NumPy, Pandas, or SciPy
- Solid understanding of data preprocessing, feature engineering, and model evaluation techniques
- Experience in ad-tech is a must, media/advertising platforms, demand-side platforms and supply-side platforms in digital advertising or retail-media networks
- Familiarity with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark) is a plus
- Problem-solving skills and the ability to think critically to develop innovative solutions
- Excellent communication and collaboration skills to effectively work with cross-functional teams and present complex findings
What you get:
- Competitive compensation, flexible remote work
- Unlimited Responsible PTO
- Great opportunity to join a growing, cash-flow-positive company while having a direct impact on Nift's revenue, growth, scale, and future success
