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Associate Software Engineer - Data Science

Devsinc is on the lookout for Data Scientists with 6 months - 1.5 years of experience, with a particular focus on machine learning (ML). This role is ideal for individuals who have begun to develop their skills in ML methodologies and are eager to apply this knowledge to solve real-world challenges. The successful candidate will demonstrate a strong foundation in ML techniques, an analytical mindset for unraveling complex data puzzles, and a dedication to contributing to data-driven decisions and innovations.

Responsibilities:

  • Design, develop, and deploy machine learning models to address specific business challenges. This includes data preprocessing, feature engineering, model selection, training, and validation.
  • Perform exploratory data analysis to uncover hidden patterns, correlations, and insights within structured and unstructured data. Utilize these findings to refine ML models and approaches.
  • Engage with a multidisciplinary team of data scientists, engineers, and business stakeholders to refine data requirements and deliver ML-driven solutions.
  • Create clear visualizations to represent the outcomes of ML models and analyses. Prepare comprehensive reports and presentations that translate complex ML concepts and findings into actionable business insights.
  • Actively pursue learning opportunities in advanced machine learning techniques and algorithms. Incorporate cutting-edge research and tools into projects to enhance model performance and efficiency.
  • Assist in the development of prototypes for predictive models and other ML applications, testing their effectiveness in real-world scenarios.
  • Cross-Functional Application of Insights, Datasets, Code, and Models: Look for opportunities to use insights/datasets/code/models across other functions in the organization (for example in the HR and marketing departments)
  • Stay curious and enthusiastic about using algorithms to solve problems and enthuse others to see the benefit of your work.
  • Maintain clear and coherent communication, both verbal and written, to understand data needs and report results.
  • Bachelor’s degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field with significant coursework or projects in machine learning.
  • A minimum of 6 months -1.5 years of experience in machine learning or data science, with a portfolio demonstrating projects in ML model development, data analysis, and feature engineering.
    Solid proficiency in ML libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and programming languages such as Python. Experience with SQL and familiarity with data visualization libraries (e.g., Seaborn, ggplot2).
  • Exceptional skills in analyzing complex data sets to develop ML models that effectively address business needs.
  • Strong ability to communicate complex ML concepts and the results of analyses clearly to both technical and non-technical stakeholders.
  • Demonstrated ability to work effectively in a team, showing adaptability and openness to feedback.
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Average salary estimate

$70000 / YEARLY (est.)
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$60000K
$80000K

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What You Should Know About Associate Software Engineer - Data Science, Devsinc

At Devsinc, we are excited to welcome new talent as we seek an Associate Software Engineer - Data Science to join our innovative team! If you have between 6 months and 1.5 years of experience and a passion for machine learning, you could be just the person we’re looking for. In this role, you will dive into the fascinating world of data and tackle real-world challenges by designing and deploying machine learning models. Your analytical mindset will help you navigate complex data puzzles, providing valuable insights to drive data-driven decisions and innovations. You’ll collaborate closely with a diverse team of data scientists, engineers, and business stakeholders, all while enhancing your skills in advanced machine learning techniques. You’ll also have opportunities to create compelling data visualizations and reports that translate intricate ML concepts into actionable insights. If you’re eager to learn and share your enthusiasm for algorithms, we want to hear from you! Join Devsinc, where you can thrive in a culture that values curiosity and teamwork. This is more than a job; it’s a chance to make a real impact in the world of data science!

Frequently Asked Questions (FAQs) for Associate Software Engineer - Data Science Role at Devsinc
What are the key responsibilities of the Associate Software Engineer - Data Science at Devsinc?

As an Associate Software Engineer - Data Science at Devsinc, your key responsibilities will include designing, developing, and deploying machine learning models to solve business challenges. You'll perform exploratory data analysis to uncover insights, engage with a diverse team to refine data requirements, and present complex findings in an understandable way. Creating clear visualizations and reports based on your analyses will also be vital to our decision-making process.

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What qualifications are needed for the Associate Software Engineer - Data Science position at Devsinc?

To qualify for the Associate Software Engineer - Data Science role at Devsinc, candidates should possess a Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, Statistics, or a related field. Additionally, candidates should have 6 months to 1.5 years of experience in data science or machine learning, showcasing proficiency in ML libraries like TensorFlow or PyTorch, programming in Python, and familiarity with SQL and data visualization libraries.

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What skills are essential for the Associate Software Engineer - Data Science role at Devsinc?

Essential skills for the Associate Software Engineer - Data Science role at Devsinc include a solid foundation in machine learning techniques, strong analytical abilities to interpret complex data sets, and excellent communication skills to convey your findings to various stakeholders. Additionally, proficiency in programming languages like Python and experience with machine learning libraries and data visualization tools are crucial for success in this position.

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How does Devsinc support the growth of its Associate Software Engineer - Data Science employees?

At Devsinc, we value continuous learning and professional growth. As an Associate Software Engineer - Data Science, you will have access to various learning opportunities, including advanced machine learning techniques and methodologies. You’ll have the chance to work on real-world projects and implement cutting-edge research, allowing you to enhance your skills and stay updated with industry trends.

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What is the work environment like for an Associate Software Engineer - Data Science at Devsinc?

The work environment at Devsinc is collaborative and dynamic, emphasizing teamwork and innovation. As an Associate Software Engineer - Data Science, you will be part of a multidisciplinary team, engaging with colleagues from different backgrounds and expertise. We encourage curiosity, adaptability, and open communication, making it a great place to grow, share ideas, and contribute to impactful data-driven solutions.

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Common Interview Questions for Associate Software Engineer - Data Science
Can you explain a machine learning project you have worked on?

When answering this question, describe the specific problem you addressed, the dataset you used, the ML techniques implemented, and the outcomes. Focus on your role in the project and highlight any challenges you faced, along with how you overcame them. Use results and metrics to showcase your contribution.

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What is your approach to data preprocessing for machine learning?

Discuss the importance of cleaning and preparing data for modeling. Mention steps such as handling missing values, normalization, and feature engineering. Provide examples of specific techniques you use and how they impact the performance of your models.

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How do you choose the right machine learning model for a problem?

Explain your thought process in selecting a model. Consider the nature of the data, the problem type (classification, regression), and the performance metrics that matter. Discuss how you evaluate and compare several models through experimental validation and cross-validation techniques.

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What tools and libraries do you frequently use for machine learning?

Mention libraries such as TensorFlow, PyTorch, or scikit-learn and why you favor them. You may also discuss your experience with data processing tools like Pandas, visualization libraries such as Matplotlib or Seaborn, and any experience with cloud services for deployment.

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Can you describe your experience with exploratory data analysis?

Share specific examples of how you use exploratory data analysis to uncover relationships or patterns within a dataset. Discuss techniques such as statistical analysis, data visualization, and how these insights can inform your modeling decisions.

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How do you handle imbalanced datasets in machine learning?

Explain the strategies you apply, such as resampling techniques (oversampling or undersampling), using weighted loss functions, or exploring alternative evaluation metrics. Highlight the importance of addressing this issue in enhancing your model’s performance.

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What are the most important metrics to evaluate a machine learning model?

Discuss metrics that are relevant based on the type of problem you’re solving. For classification, you might mention accuracy, precision, recall, and F1 score. For regression, explain metrics like mean absolute error or R-squared. Explain why choosing the right metric is crucial.

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How do you ensure the quality of your machine learning models?

Talk about the importance of overfitting and how you prevent it through cross-validation and regularization techniques. Mention continuous model monitoring after deployment to ensure that it performs well with new data.

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Describe a situation where you had to communicate complex technical findings to a non-technical audience.

Provide an example that showcases your ability to simplify your analysis and findings. Discuss the methods you used, like visualizations or analogies, to ensure clarity and understanding among diverse audiences.

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What role does teamwork play in your approach to data science?

Emphasize the importance of collaboration in data science. Discuss instances where team input improved your project outcomes and how you value diverse perspectives in problem-solving. Highlight your adaptability in a team setting.

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Full-time, remote
DATE POSTED
April 8, 2025

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