Data Scientist/ Sr. Data Scientist

Jaspercolin; India; On-site; INR 700,000 - 1,500,000; Full time

Benefits offered by this job

Attractive salary

Benefits and performance-based incentives

Continuous learning opportunities

Work-life balance

Job summary

A leading data analytics firm in India is looking for a talented Data Scientist to join their team. In this pivotal role, you will engage in planning, executing, and delivering machine learning projects across various domains including marketing and finance. The ideal candidate should have expertise in machine learning and data preparation, along with the ability to collaborate effectively with cross-functional teams. This opportunity offers competitive compensation and a chance to shape the future of data science within the organization.

Qualifications

• 3-5 years of experience in data science with a proven track record of projects.
• Experience in building and deploying predictive models and data pipelines.
• Strong knowledge of data mining and statistical techniques.

Responsibilities

• Plan and execute machine learning-based projects.
• Work with IT and business units to align on project objectives.
• Implement and test various machine learning models.

Skills

Machine Learning

Deep Learning

Data Preparation

Statistical Modeling

Cross-functional Collaboration

Education

Bachelor’s degree in Computer Science, Data Science, or related field

Master’s degree preferred

Tools

Python

Jupyter Notebooks

Azure Machine Learning

Snowflake

Google Cloud ML

Job description

We are looking for a talented and highly motivated Data Scientist to join our Data and Analytics Platform Team. This is a critical role where you will be responsible for planning, executing, and delivering machine learning-based projects across various domains, including marketing analytics, supply chain optimization, and finance. You will play an integral part in leveraging advanced data science and machine learning techniques to uncover insights, drive business strategy, and optimize operational efficiency.The ideal candidate will have a strong background in machine learning, deep learning, and statistical modeling, along with experience in data integration, data preparation, and operationalization. You will work closely with cross-functional teams, including the IT department, business units, and the analytics team, to ensure that data-driven insights are effectively communicated and implemented. As a key player in the organization, you will also guide and inspire teams across the business in leveraging artificial intelligence and machine learning to drive innovation and performance.

Key Responsibilities:

Problem Analysis & Project Management:

Business Potential & AI Strategy:

• Identify business opportunities where data science and machine learning can create significant value.
• Guide the organization in understanding the strategic potential of AI and machine learning to transform business operations.
• Work closely with IT, business units, and analytics teams to understand constraints and align on project objectives.
• Prioritize, scope, and manage data science projects, ensuring alignment with business goals and KPIs.

Governance & Compliance:

• Define and communicate governance principles for data science projects, ensuring that ethical standards and regulatory compliance are met.

Data Collection & Integration:

• Understand and acquire access to various data sources (e.g., SQL, graph databases), building data pipelines to support efficient and repeatable data science workflows.
• Document and catalog data pipelines for clarity and transparency across teams.

Data Integration:

• Ensure integration of diverse data sources for cohesive project execution and seamless analytics.

Data Exploration & Preparation:

• Apply techniques like hierarchical clustering, principal components analysis (PCA), and other statistical methods to explore and understand data.
• Hypothesis Generation & Testing:
  • Generate hypotheses regarding business processes, test using quantitative methods, and present actionable findings.
• Business Understanding:
  • Collaborate with domain experts to gain deep insights into business mechanics and incorporate those insights into data analysis.

Machine Learning & Advanced Analytics:

• Apply machine learning and advanced analytics techniques to perform classification, prediction, and optimization tasks.
• Implement and test various models such as regression models, random forests, neural networks (ANN, RNN, CNN), support vector machines (SVM), and deep learning models.
• Conduct cross-validation, A/B testing, and other model validation techniques to assess the performance, fairness, and bias in models.
• Domain Integration:
  • Leverage domain expertise (e.g., finance, marketing, supply chain) to integrate business knowledge into ML solutions.
• Collaboration with Engineers:
  • Work with ML Data Engineers to evaluate and implement ML deployment options, ensuring smooth integration of models into production environments.
• Model Performance Management:
  • Implement monitoring tools to track model performance and health, ensuring continued optimization and accuracy.
• Conduct champion/challenger testing (e.g., A/B tests) on live systems to measure model impact and effectiveness.

Other Responsibilities:

• Training & Knowledge Sharing:
  • Provide training to business and IT staff on basic data science principles and advanced techniques.
• Promote collaboration between data science teams within the organization and across external partnerships.
• Agile Delivery Methodology:
  • Lead data science initiatives within an agile framework, ensuring iterative and incremental delivery of data solutions.

Qualifications:

Education:

• Bachelor’s degree in Computer Science, Data Science, Operations Research, Statistics, Applied Mathematics, or a related field required. Master’s degree preferred.
• Specialization in machine learning (ML), artificial intelligence (AI), cognitive science, or data science is highly desired.

Experience:

• At least 3-5 years of experience in data science, with a proven track record of launching, planning, and executing data science projects.
• Experience building and deploying predictive models, web scraping, and scalable data pipelines in real-world applications.

Technical Skills:

• Expertise in Python, Jupyter Notebooks, and familiarity with other scripting languages such as R, SAS, or MATLAB.
• Proficient in data science platforms such as Azure Machine Learning, Snowflake, and Google Cloud ML. Experience with other platforms like AWS or SAS is a plus.
• Strong knowledge of statistical and data mining techniques, including generalized linear models, regression analysis, random forests, boosting methods, text mining, deep learning, and neural networks.
• Experience with open-source libraries (e.g., sklearn, TensorFlow, PyTorch) and data visualization tools (e.g., Plotly, Streamlit, Power BI, Tableau) is a plus.
• Solid understanding of database programming (SQL) and familiarity with NoSQL and graph databases.

Additional Skills:

• Familiarity with MLOps and ModelOps to operationalize ML models in production environments.
• Experience in implementing deep learning techniques such as ANN, RNN, CNN, and SVM.
• Strong ability to design and implement demand forecasting models, predictive maintenance, and fraud detection models.

Leadership:

• Strong leadership skills, with the ability to motivate and guide cross-functional teams, including both technical and non-technical stakeholders.

Analytical & Strategic Thinking:

• Excellent analytical thinking, problem-solving abilities, and the capacity to translate complex data insights into actionable business strategies.
• Ability to collaborate across teams and effectively communicate technical concepts to both technical and non-technical audiences.

Agility & Flexibility:

• Comfortable working within an agile delivery methodology and managing fast-paced, evolving requirements.
• Motivated by achieving long-term business outcomes and willing to take ownership of key initiatives from ideation to implementation.

What We Offer:

• An opportunity to shape the future of data science at our organization, with a chance to lead high-impact projects in marketing, supply chain, and finance analytics.

Collaborative Environment:

• Work alongside cross-functional teams in a dynamic, fast-paced environment, fostering innovation and creative problem-solving.

Competitive Compensation:

• Attractive salary, benefits, and performance-based incentives, along with a culture of work-life balance.

Learning & Development:

• Continuous learning opportunities and the ability to work with cutting-edge tools and technologies in machine learning and AI.

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