Data Scientist - Delhi NCR, India - Datawrkz

Description

Key Responsibilities :

- Lead a team of software developers, including hiring, training and mentoring.

- Provide technical guidance and direction to the team, including coding standards, architectural.

Principles, and development best practices, including appropriate documentation and review.

- Collaborate with the Product Manager and other stakeholders to define project requirements, scope, and timelines.

- Participate in the design and development of software solutions, ensuring they meet quality and performance standards.

- Monitor project progress, identify and mitigate risks, and communicate project status to stakeholders.

- Identify technical debt and provide recommendations to address it.

- Evaluate new technologies and tools, and recommend their adoption where appropriate.

- Foster a culture of continuous improvement, innovation, and collaboration within the team.

Skills and Experience :

- Bachelor's or Master's degree in Computer Science or related field.

- 4+ years of experience in software development, with a focus on backend or full-stack development.

- 2+ years of project management experience, leading a team of software developers.

- Strong experience with Python, Django or other frameworks.

- Experience with web development frameworks such as React,Angular.

- Experience with cloud based solutions, deployment etc.

- Experience with Agile development methodologies, such as Scrum or Kanban.

- Strong communication and leadership skills.

- Strong problem-solving and analytical skills

Responsibilities :

Data Analysis and Modeling :

Conduct exploratory data analysis to identify trends, patterns, and insights.

Develop and implement machine learning models and algorithms for predictive and prescriptive analytics.

Utilize statistical methods to extract meaningful information from large datasets.

Data Cleaning and Preprocessing :

Clean, preprocess, and validate raw data to ensure accuracy and completeness.

Handle missing data and outliers, and implement strategies for data imputation.

Feature Engineering : Identify relevant features and engineer new ones to enhance model performance. Collaborate with domain experts to incorporate domain knowledge into feature selection.

Model Evaluation and Optimization : Evaluate model performance using appropriate metrics. Fine-tune models and algorithms to improve accuracy, precision, and recall.

Data Visualization : Create visualizations to communicate complex findings and results effectively. Use tools such as Matplotlib, Seaborn, or Tableau for data visualization.

Collaboration : Work closely with cross-functional teams, including business stakeholders, to understand business objectives and requirements. Communicate findings and insights to non-technical stakeholders in a clear and understandable manner.

Research and Development : Stay updated on the latest developments in data science and machine learning. Contribute to the research and development of new algorithms and methodologies.

Data Privacy and Ethics : Ensure compliance with data privacy regulations and ethical considerations in handling sensitive information.