Data Scientist at Typeform

Hi! Before diving into the job details, let's give you some context on who we are and what to expect when joining the team.
Typeform, launched in 2012 in Barcelona, drives more than 500 million digital interactions per year and integrates with hundreds of other business-critical tools. We’re expanding from a small Barcelona-based business to a truly international and remote company. We’re hiring talent across Spain, the United States, the United Kingdom, Germany, Colombia, France, Ireland, the Netherlands and Portugal. Be aware, the location is to be subjected to change, depending on the role.
Our vision is a world of more personal business relationships. Through our no-code Saas platform, we believe we can create that world by living our mission: To bring people closer with better conversations. We turn digital interactions into human connections, by offering people-friendly forms, quizzes, surveys, and asynchronous video solutions.
To do so, we look for people who are curious, ready to own their objectives, and passionate about taking organisations to their next chapter. But we are aware it’s not for everyone, our environment is perfect for those willing to become a change agent and roll up their sleeves to build our rocket ship.
 About the Team
You will be joining the Data Science team in the Data and Insights division at Typeform. As part of this team, you will report to the "Head of Data Science" and collaborate closely with a team consisting of 3 data scientists, 1 machine learning engineer, and 1 manager. Currently, the team comprises 2 data scientists and a manager, but it is designed to have a total of five members, including the new positions. With the addition of an extra data scientist and an additional machine learning engineer, the team will achieve its intended composition of 3 data scientists, 1 machine learning engineer, and 1 manager. The team is located in the EMEA region.
As a data scientist in this team, your primary focus will be on solving complex data-related problems and challenges. You will analyze large datasets, derive insights, build predictive models, and create data-driven solutions to improve various aspects of Typeform's products, services, and operations.
About the Role
The role of a data scientist will primarily solve complex data-related problems and challenges. This includes tasks such as analyzing large datasets, deriving insights, building predictive models, and creating data-driven solutions to improve various aspects of Typeform's products, services, and operations.
The strategic purpose is to leverage data as a strategic asset for the organization. By applying advanced analytics and machine learning techniques, the DS helps drive strategic decision-making, identify trends and patterns, uncover business opportunities, and optimize processes across different functions.
Things you will do:


Collaborate with cross-functional teams to understand and translate business objectives into data-driven solutions.


Work on specific projects and initiatives to improve user engagement, personalization, conversion rates, or operational efficiency.


Utilize data analysis, modeling, and experimentation techniques to drive insights and deliver tangible outcomes.


Collaborate closely with a machine learning engineer to leverage their support and expertise.


Conduct thorough data analysis to identify patterns, trends, and opportunities for optimization.


Develop and implement statistical models and machine learning algorithms to extract valuable insights from data.


Design and execute experiments to test hypotheses and validate proposed solutions.


Generate actionable recommendations based on data findings and insights.


Communicate results and recommendations to stakeholders effectively, both verbally and through data visualization.


Continuously monitor and evaluate the performance of data-driven solutions and iterate as needed to ensure ongoing improvement.


Stay up-to-date with the latest advancements in data science and machine learning to bring innovative approaches to problem-solving.


What you already bring to the table:


Statistical Analysis: Proficiency in statistical techniques, hypothesis testing, regression analysis, and experimental design to extract insights from data.


Machine Learning: Strong understanding and practical experience with machine learning algorithms, including classification, regression, clustering, dimensionality reduction, and ensemble methods.


Data Manipulation and Cleaning: Expertise in data preprocessing, cleaning, and transforming techniques to ensure data quality and integrity.


Programming Languages: Proficiency in programming languages such as Python for data manipulation, analysis, and model implementation.


Data Visualization: Ability to effectively communicate insights through data visualization techniques.


SQL and Database Knowledge: Familiarity with querying databases using SQL and understanding relational database concepts.


Data Mining and Exploration: Competence in exploratory data analysis (EDA), data mining techniques, and feature engineering to uncover patterns and hidden insights in data.


Experimental Design: Understanding of experimental design principles to design and conduct controlled experiments or A/B tests for data analysis.


Software and Tools: Proficiency in using data science libraries and frameworks like NumPy, Pandas, or scikit-learn, for efficient data analysis and model development.


Big Data and Cloud Computing: Familiarity with big data frameworks like Spark and cloud platforms like AWS for handling large-scale datasets


Extra awesome:


Natural Language Processing (NLP): Knowledge of NLP techniques for text analysis, sentiment analysis, language modeling, or chatbot development.


Deep Learning: Experience with deep learning architectures, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and related frameworks like Keras or TensorFlow.


Time Series Analysis: Proficiency in analyzing and forecasting time series data using methods like ARIMA, SARIMA, or LSTM.


Unsupervised Learning: Familiarity with unsupervised learning techniques like clustering, anomaly detection, or association rule mining.


Natural Language Generation (NLG): Proficiency in generating human-like language output from data using NLG techniques.


*Typeform drives hundreds of millions of interactions per year, powering conversational, human-led experiences all over the world. We are proud to be an equal-opportunity employer. We celebrate diversity and do not tolerate discrimination and harassment of any kind, regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We believe that by celebrating our differences, we can win together.