Data Science Developer Hiring Guide

Data science is the practice of extracting value from data through artificial intelligence (AI), machine learning, and statistics. Using data science tools, companies can generate valuable insights that can be used to make better decisions and optimize existing products and services.

The data science process has many components: data mining, data cleaning, exploration, predictive modeling, data analysis, and data visualization. Data scientists use different languages ​​and tools such as Python, Java, R, and SQL to create project pipelines that best meet requirements. Companies also use Apache Spark for big data and Tableau/Datapine for business intelligence and visualization.

Many organizations use automation tools to capture and comb through large sets of data. Version control tools are used to mark changes to the project and track modified data. Finally, the data is sent to data engineers/scientists who clean and pre-process the data. They remove duplicate or irrelevant entries and filter out outliers. They may also need to deal with missing data.

Hiring Guide

After proper processing, data scientists perform hypothesis testing and predictive modeling through machine learning algorithms. To fully understand data and generate insights, it may also need to include statistics and probabilities. The algorithms used in this phase include decision trees, linear and logistic regression, classification and XGBoost.

They may also need to use SQL queries to join data across databases such as MySQL and PostgreSQL. The last step is the presentation of the data. This is done through graphs and reports. Engineers use data visualization tools like Tableau and R Studio to create dashboards and produce reports

Data science in today's market

Nowadays, data science is an integral part of the decision-making process of organizations. Its popularity has grown over the years, with several companies funding and implementing data science projects. Even during the COVID-19 lockdown when most businesses were affected, companies invested heavily in data and decision sciences.

Data science projects improve the effectiveness of existing applications by generating a diverse set of insights about customers, markets, and businesses. They can be used to create recommendations and detect fraud. Furthermore, data science also aids companies' branding and marketing initiatives by segregating highly specific consumer groups for laser-precision campaigns.

Problems companies face when hiring data science engineers

Although data science is a thriving field, companies still have difficulty hiring data science engineers/scientists. There is a huge skills gap in the industry. One reason for this is the amount of work required just to stay in the field. Data science requires a lot of skills and specialization, and many engineers cannot keep up with the constant training.

Another big problem companies face when hiring data scientists is inexperience in cleaning data. Data scientists spend a lot of time cleaning and pre-processing data. It means cleaning up inaccurate, duplicate, incomplete, and inconsistent entries. This requires a lot of patience and experience, as well as business knowledge, which many candidates lack.

How to Select the Perfect Data Science Engineer?

Although selecting a data scientist may seem difficult, there are certain things you can check before hiring data scientists. Potential candidates must possess statistical and probability knowledge and have experience with machine learning.

They must also have experience in data engineering and visualization tools. They must be well versed in SQL and query handling. Candidates with knowledge of big data tools such as Apache Spark should be preferred.

Finally, data visualization is an important part of data science projects. Choose the candidate who has experience in Tableau and R. He should be able to generate boxplots and scatterplots along with heatmaps and trees.

Interview Questions

What is the purpose of A/B testing?

A/B testing is a randomized test that compares 2 variables and observes their effect on the overall product. This testing allows a company to collect and study data, record results, and change its current processes. Most industries use it to determine the direction their product should take.

What is supervised learning?

Supervised learning is a category of machine learning where algorithms are trained with labeled data.

The algorithm trains on the input data. Once sufficiently trained, the algorithm can predict values ​​for data outside the training data set, i.e. new values. Supervised learning allows an algorithm to predict an output based on previously analyzed and processed data.

State differences between regression and classification

In data science, classification is the task of predicting a specific class label. The algorithm identifies the output category of the data and classifies it into those categories. This is used to segregate data into discrete values.

Regression is the practice of speculating a continuous quantity through known data. The algorithm takes the input and generates continuous values ​​using the line of best fit. Regression problems with more than one output variable are called multivariate regression problems.

Why is Naive Bayes called naive?

Naive Bayes is a practical algorithm for predictive modeling. It is called naive because it infers that each input variable is autonomous. This assumption is often wrong and doesn't work for real-world data, hence the naive label.

What do you understand about the random forest algorithm?

A random forest algorithm is a machine learning algorithm based on decision trees. A random forest model is created by combining many decision trees through bagging.

Random forest is much more effective than decision trees for managing bulk data. It can solve overfitting problems in decision trees and generate results with low bias and variance.

Job description

We are looking for highly qualified and experienced data science professionals to design and implement machine learning models. They must have experience in Python and R and be able to handle big data through Hadoop.

The candidate must have good communication skills and be able to work on different aspects of data science projects, i.e. data preprocessing, cleaning, ETL, modeling, data visualization and reporting. Additionally, they must work in a team and be able to collaborate with different teams on different projects.

Responsibilities

• Design, develop and deploy data-driven systems and architecture.
• Work on data processing pipelines.
• Develop code to create and deploy machine learning/AI models.
• Work on project features and optimize classifiers.
• Perform data extraction, transformation, and loading (ETL)
• Implement data science use cases on Hadoop
• Work on data cleaning and standardization.
• Work on deep learning models and algorithms such as CNN and RNN.
• Work collaboratively with different stakeholders.
• Resolve bugs and apply maintenance.
• Follow industry best practices and standards
• {{Adicione outras responsabilidades relevantes}}

Skills and qualifications

• Knowledge of data science toolkits such as Scikit-learn, R, Pandas, NumPy, Matplotlib.
• Previous experience writing and executing complex SQL queries
• Deep understanding of machine learning techniques and algorithms such as classification, regression, random forest, and decision trees.
• Experience with code versioning and collaboration tools.
• High proficiency in Python/Java/C++.
• Candidates with data visualization experience are preferred.
• Knowledge of big data tools (Spark, Flume) is an advantage.
• {{Adicione outras estruturas ou bibliotecas relacionadas à sua pilha de desenvolvimento}}
• {{Liste o nível de escolaridade ou certificação necessária}}

Conclusion

Data science plays a key role in today's industry and is growing rapidly. Many industries such as telecommunications, healthcare, retail, e-commerce, automotive, and digital marketing use data science to improve their services. As a business owner, it makes sense to invest in data science for your decision-making process. It improves risk management and greatly improves accountability.