Data Scientist- ML/Model Development
Data Science Team

Nift is disrupting performance marketing, delivering millions of new customers to brands every month and we are actively looking for a Data Scientist, who will focus on Model developmentWe are a well-managed, data-driven, cash-flow-positive company with a customer-first mindset. Our investors are those who invested in Fitbit, Warby Parker, Wayfair, and Twitter..

The role:

As a Data Scientist reporting to our head of Data Science, you will play a crucial role in developing models for big and 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. .

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 understanding of 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, regularized regression
  • 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.
  • Hands-on experience with machine learning frameworks and libraries, such as TensorFlow, PyTorch, or sci-kit-learn.
  • Solid understanding of data preprocessing, feature engineering, and model evaluation techniques.
  • Familiarity with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark) is a plus.
  • Strong 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.