Data Scientist - Operations

As a Data Scientist - Operations at Mochi, you will be responsible for the entire lifecycle of operational metrics. Your duties include designing KPIs, deploying dashboards, automating workflows using Python and APIs, and utilizing tools like Tableau or Streamlit based on audience needs. You will also leverage techniques like causal inference and A/B testing to guide decision-making and streamline processes while integrating innovative technologies like Generative AI.

To qualify for the Data Scientist - Operations role at Mochi, candidates are expected to hold a Bachelor’s or Master’s degree in a quantitative field such as Statistics or Engineering. Additionally, you should have at least one year of experience applying data solutions to product, marketing, or business challenges. Proficiency in Python and SQL, along with experience in dashboard building and a knack for clear communication, are crucial for success in this position.

The work environment at Mochi for a Data Scientist - Operations is dynamic and fast-paced. You'll be part of a high-growth team known for its collaborative spirit and innovative mindset. Our office is located in downtown San Francisco, providing a vibrant setting where you can engage with top engineers, product leaders, and operations experts, fostering both personal and professional development amid meaningful connections and community-building activities.

In the Data Scientist - Operations role at Mochi, you will utilize various technologies including Python for automation and data analysis, SQL for querying information, and data visualization tools like Tableau and Streamlit for presenting insights. You will also be integrating Generative AI and working with APIs to enhance operational workflows, making your toolkit diverse and cutting-edge.

Mochi supports the professional growth of Data Scientists through a culture of learning and collaboration. You will have opportunities to engage in high-impact projects that challenge your skills, access resources for personal development, and participate in group activities that foster knowledge sharing. Additionally, the competitive compensation package, including equity, reflects our appreciation for your contributions and helps you grow in a meaningful way.

Certainly! Discuss specific projects where you identified high-friction workflows and successfully implemented automation solutions. Highlight your use of Python and APIs to streamline processes, along with any measurable outcomes, like time savings or efficiency improvements. This illustrates your hands-on experience and problem-solving abilities in real-world scenarios.

When designing KPIs, I focus on aligning them with the business objectives. I begin by identifying the key outcomes we want to measure, then work collaboratively with stakeholders to define what success looks like. It's important to develop metrics that are not only measurable but also actionable, guiding teams to address inefficiencies and improve decision-making over time.

I am proficient in a variety of tools, such as Python for coding and algorithm development, SQL for data extraction and manipulation, and Tableau or Streamlit for data visualization. Each tool has its strengths, and my expertise allows me to choose the right one based on the project's needs and the audience's familiarity with the platform.

In a previous role, I created a presentation that showcased complex data insights to a marketing team. By using visualizations and analogies, I broke down the data into relatable concepts. This not only helped them understand the analysis but also enabled them to adopt strategies based on the insights I presented, demonstrating the importance of clear communication.

I thrive in environments with ambiguity by maintaining a flexible mindset and prioritizing clear communication. I tackle challenges by breaking them into smaller, manageable parts, ensuring I stay focused while iterating on solutions. I also regularly check in with colleagues to gather insights and foster collaboration, which helps uncover clarity amidst uncertainty.

I have substantial experience creating data visualizations, particularly using tools like Tableau and Streamlit. For example, I built dashboards that visually represented key operational metrics, enabling stakeholders to quickly grasp performance trends. My approach emphasizes clarity and intuitiveness, ensuring that the data tells a compelling story and drives informed decision-making.

I evaluate the impact by establishing metrics for success at the onset of every project. This could include measuring efficiency improvements, increases in revenue, or user engagement. Post-implementation, I conduct analyses to compare outcomes against these metrics. Gathering feedback from stakeholders also helps assess the qualitative impact of my work in real-world applications.

In my previous roles, I have employed causal inference techniques and A/B testing to evaluate the effectiveness of product features and marketing strategies. I design experiments that allow for controlled comparisons, carefully analyzing data to draw conclusions about causal relationships, which drives informed decision-making in future projects and strategies.

I am driven primarily by the desire to solve complex problems and make a meaningful impact. The thrill of discovering insights from data and transforming them into actionable solutions keeps me motivated. Additionally, I value collaboration with diverse teams, as it fosters creativity and innovation, allowing us to tackle high-leverage challenges together.

I keep up with the latest trends in data science by regularly engaging with relevant literature, participating in online courses, and attending industry conferences. Staying active in data science communities on platforms like GitHub and LinkedIn also allows me to exchange ideas and learn from peers, ensuring that my knowledge and skills remain current in this rapidly evolving field.