Senior Data Scientist (USA / Israel) - nift

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 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 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

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 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 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

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 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 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