Senior Analyst – Data Scientist

As a Senior Analyst – Data Scientist at METRO Global Solution Center, your responsibilities include conducting exploratory data analysis, implementing machine learning algorithms, developing predictive models, cleansing and transforming datasets, and creating visualizations to support decision-making. You will also collaborate with business leaders to align projects with strategic objectives and ensure the models deployed in production are monitored for performance and accuracy.

To apply for the Senior Analyst – Data Scientist position at METRO Global Solution Center, you should possess a Bachelor’s degree in computer science, IT, Data Science, or a related field, along with over 5 years of experience in a similar data science role. Proficiency in programming languages like Python and SQL, as well as familiarity with data visualization tools, is essential. Strong analytical and problem-solving skills are crucial, coupled with the ability to translate business needs into technical specifications.

Essential technical skills for the Senior Analyst – Data Scientist role at METRO Global Solution Center include proficiency in data mining, statistical analysis, predictive modeling, and advanced data visualization. Candidates should have hands-on experience with LLMs, and familiarity with tools and languages such as R, Python, SQL, Tableau, and GCP cloud services like BigQuery, is highly desired.

At METRO Global Solution Center, we believe in continuous growth and learning. As a Senior Analyst – Data Scientist, you will have access to training and development opportunities that expand your skills and knowledge in data science and related fields. Additionally, you will work on a variety of projects across different domains, allowing you to gain diverse experiences that contribute to your professional growth.

Senior Analysts – Data Scientists at METRO Global Solution Center enjoy a stable employment environment within a growing international corporation. Benefits include private medical care, group insurance, cash bonuses from the Social Benefits Fund, a language skill supplement, co-financed sports cards, and opportunities for team building and integration events. We also support a flexible work-life balance, including options for remote work.

When answering this question, discuss specific machine learning algorithms you have used in previous projects, detailing how you chose them based on the business problem you were solving. Mention any frameworks you utilized, such as TensorFlow or Scikit-learn, and share examples of how your model impacted decision-making.

Discuss the various techniques you've employed to handle missing data, such as imputation methods, mean or median substitution, or removing data points entirely, depending on the context of the analysis. Highlight your ability to assess the impact of missing data on overall outcomes.

Mention the data visualization tools you've used, like Tableau or matplotlib, and provide examples of how you used these tools to create impactful visualizations. Emphasize the importance of storytelling with data to facilitate understanding among non-technical stakeholders.

Share a specific example where you developed a predictive model, focusing on the problem it addressed, your approach to creating the model, and the results it generated. Discuss the metrics used to evaluate success and any lessons learned in the process.

Detail the data cleaning and preprocessing steps you follow to ensure data quality, including outlier detection, consistency checks, and validation against known benchmarks. Mention any tools or programming languages that aid in this process and the importance of data integrity for accurate analyses.

Highlight your communication skills and how you gather requirements from business units. Discuss your experience in translating complex analytics into business insights, and how you prioritize projects based on strategic goals, fostering a collaborative atmosphere.

Choose a specific challenge, explain the context, and outline the steps you took to overcome it. Discuss the key takeaways and how this experience enhanced your problem-solving skills or refined your analytical processes.

Share your methods for staying current in the field, such as participating in webinars, attending industry events, and following relevant blogs or journals. Highlight specific resources you find valuable and how they inform your work as a data scientist.

Discuss specific cloud platforms you have worked with, such as GCP or AWS, and how they support data storage, management, and analysis. Share your experience in deploying models on the cloud and the benefits of scalability and accessibility for data science projects.

Emphasize the importance of exploratory data analysis in identifying patterns, spotting anomalies, and defining hypotheses. Discuss how you've used EDA in past projects to guide your analyses and refine your models, driving toward actionable insights.