Daniela Prass Email and Phone Number

As an entry-level data scientist, I had the privilege of working on a pivotal Tax Finance project for the Department of Finance in the state of Rondônia. In this role, I undertook an exhaustive integrity data analysis of invoices and tax declarations sourced from a prominent Energy company. My responsibilities extended to meticulously validating business rules and pioneering initial analyses in the client's newly established big data environment. During this engagement, I played a crucial role in identifying potential data issues that could have posed challenges during the data ingestion phase for production. This proactive approach not only ensured data accuracy but also set the foundation for a streamlined and successful project execution.Currently, I'm actively involved in a Chat GPT study group, where I'm deepening my understanding of this fascinating field and actively contributing to the creation of internal learning materials. It's an exciting opportunity for me to expand my knowledge and expertise in the world of AI and natural language processing.

Embracing the role of a mid-senior data analyst, I thrived in a dynamic Big Data environment, collaborating seamlessly with cross-functional teams comprising Data Engineers, Business Managers, and Developers. To harness the full potential of Apache Spark's performance, I eagerly took on the challenge of mastering a new programming paradigm, adopting a distributed thinking approach over a sequential one.Through this journey, I honed my expertise in a diverse range of technologies including SQL, Amazon Web Services (AWS), Git, and PySpark, while actively contributing to the development of robust data models. Notably, I spearheaded a tax compliance analysis initiative, successfully creating key performance indicators and enhancing a government program, Pai d'Égua, aimed at rating taxpayers.One of my proudest achievements was devising an innovative e-commerce fraud detection logic, leveraging statistical indicators derived from comprehensive outlier analyses. This not only showcased my analytical skills but also delivered tangible value to the client.

After completing my graduation and stepping into the role of an Entry-Level Data Analyst, I was entrusted with a challenging mission: overseeing the progress of up to 6 teams from various departments at Claro as they embarked on their first analytics projects using the R language. This endeavor required me to dive into R myself. It was not only about grasping a new language but also managing a high volume of parallel projects. Through this experience, I gained valuable insights into time management, got a full view of how end-to-end analytics projects function, honed my communication skills, and dipped my toes into SQL.Following that, I had the opportunity to join a significant project for the Secretary of Finance in Ceará. Initially, the team needed to craft two dashboards to showcase crucial findings to the client. In less than a month, I delved into Power BI and successfully produced visuals that left not only my team but also the client impressed. It marked my first real foray into using a visualization tool beyond Python's graphic libraries. I was so captivated by this that I even pursued a Tableau course afterward.

As an intern, my initial challenge revolved around assessing data integrity issues within gas measurement data provided by Comgas. At the time, I was still in the process of learning how to code in Python, which made the task initially daunting. However, with the guidance of my technical mentors, I managed to develop basic programming skills and started applying fundamental concepts in data exploration.Subsequently, I took part in a proof of concept project focused on barley crop optimization for Ambev. The primary objective was to discern the key characteristics that defined a high-quality batch and identify the events that influenced its quality. During this project, I had my first encounter with the Geopandas library, which I utilized in conjunction with climate data obtained from an API to contribute to various aspects of the analysis.Finally, I served as the only data analyst in a project for the Secretary of State and Economy in the Federal District. In this capacity, I produced several exploratory analyses and delivered a taxpayer rating based on compliance indicators upon project completion. To accomplish this, I primarily leveraged the pandas and plotly express libraries. I gained valuable insights into modularization during this process.

To fulfill my recently found dream to become a data scientist, I started studying by myself. I took a few online courses and enrolled in a machine learning post-graduation discipline at my university. I also discovered the existence of the computational intelligence chapter, which instantly aroused my interest. I was approved in the selection process and scrolled through a ML trail of learning. I never got to implement a real project as a member of the chapter because I graduated before I got the chance to do it. But I learned the fundamental concepts of supervised and unsupervised learning. What I couldn't do as a member of the chapter, I did as a graduate student. I was able to put some of the stuff I learned into practice in the form of my end-of-course project which was a fake news classifier using NLP techniques and support vector machines.

At Huawei, I had incredible professional growth. From the start, I received a lot of responsibilities and autonomy. I worked with mobile network infrastructure, in the engineering department. I had to plan activities and make sure our teams did the preliminary survey of telecom sites in time and with high quality. I developed my managing skills and discovered I liked to be in a managing position. I also had to validate and, sometimes, elaborate technical documents to serve as a reference for future installments. The client's engineering team had to approve the documents I delivered, so I worked closely with them to make sure the requirements were met with satisfaction. By doing this, I improved my analytical thinking and sharpened my eye to catch inaccuracies. Besides my usual assignments, I was always looking for opportunities to do some coding, because I have always had a taste for it since I learned the C language at the beginning of my graduation. I found it in some manual internal processes that used to take a lot of time. So I learned VBA and automated a few of them. My proactivity and fast learning skills were recognized through an 'outstanding intern' certificate. At some point, I had my first contact with BI. Thanks to it, I stumbled into the data analytics world, which ended up being a life-changing event. I dived into researching about it and found it to be exactly what I wanted to work with. I decided I wanted to be a data scientist and so, I started looking for ways to do it.

My time at Telebras, while brief, was quite fruitful. During my tenure, I was responsible for overseeing installment procedures and ensuring their validation. This experience allowed me to gain valuable understanding into the workings of a government organization.

In my very first professional experience, I had the exciting opportunity to immerse myself in the dynamics of a real organization. It was a significant milestone that instilled in me a profound sense of teamwork and accountability. My primary responsibility involved resolving tickets related to web domains, which provided me with an invaluable introduction to cutting-edge technologies like Docker and Rancher. This experience filled me with enthusiasm for my future career growth.