Lead Data Analytics Engineer

We are seeking a highly skilled and innovative Lead Data Analytics Engineer to join our dynamic team. This role is instrumental in leveraging data science, machine learning, and statistical modeling to influence strategic decisions and drive business outcomes. The ideal candidate will collaborate with cross-functional teams and deliver actionable insights, with the opportunity to transition into a Data Scientist role based on performance and capability.

Location : Pune

Contract : 6 Months

Roles and Responsibilities

• Data Analysis and Modeling:
• Analyze large and complex datasets to extract actionable insights that can drive business growth and decision-making.
• Develop and implement predictive models, machine learning algorithms, and statistical models to solve business problems.
• Use techniques such as regression, clustering, classification, natural language processing (NLP), and time series analysis to build robust models.
• Continuously monitor and refine models to ensure their accuracy and effectiveness over time.

Data Preparation and Feature Engineering:

• Clean, transform, and preprocess data to ensure high-quality inputs for modeling.
• Perform feature engineering to enhance model performance.
• Collaborate with data engineering teams to build efficient data pipelines that feed into machine learning models.

Experimentation and A/B Testing:

• Design, conduct, and analyze A/B tests and other experiments to evaluate the impact of different strategies and interventions.
• Provide recommendations based on experimental results to build ideal customer persona, optimize marketing campaigns, product features, and other business initiatives.

Collaboration with Cross-Functional Teams:

• Work closely with product managers, data engineers, and business stakeholders to understand their needs and translate them into data science projects.
• Communicate complex technical concepts and findings to non-technical stakeholders in a clear and concise manner.
• Provide data-driven recommendations to support strategic decisions and optimize business processes.

Data Visualization and Reporting:

• Create compelling data visualizations and dashboards using tools like Power BI, Tableau, or Python libraries (e.g., Matplotlib, Seaborn) to effectively communicate insights.
• Develop automated reporting solutions to monitor key performance indicators (KPIs) and business metrics.

Research and Innovation:

• Stay current with the latest developments in data science, machine learning, and artificial intelligence.
• Proactively propose innovative approaches and techniques to solve business problems and improve decision-making processes.
• Mentor junior data analysts and provide technical guidance on best practices.

Data Governance and Compliance:

• Ensure data governance and security best practices are followed in all data science activities.
• Work with the data engineering and IT teams to ensure compliance with regional and international data privacy regulations (e.g., GDPR, PDPA).

Skills and Qualifications:

• Strong proficiency in Python, R, or similar programming languages for data analysis and machine learning.
• Expertise in machine learning frameworks and libraries such as Scikit-Learn, TensorFlow, PyTorch, or XGBoost.
• Experience with data visualization tools such as Tableau, Power BI, or Python libraries (Matplotlib, Seaborn).
• Proficiency in statistical modeling, data mining, and predictive analytics.
• Solid understanding of data engineering principles, including data cleaning, transformation, and feature engineering.
• Experience with cloud platforms (AWS, Azure, GCP) and big data tools (e.g., Apache Spark, Databricks).
• Strong problem-solving and analytical skills.
• Ability to work independently and as part of a team.
• Excellent communication and interpersonal skills.
• Bachelor's or Masters degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.

Preferred Qualifications

• Knowledge of natural language processing (NLP) and deep learning techniques.
• Certifications in data science, machine learning, or cloud platforms.
• Experience with SQL and NoSQL databases (e.g., MySQL, MongoDB, Cassandra).
• Familiarity with data governance and compliance frameworks.

Technologies

• Programming Languages: Python, R.
• Machine Learning Frameworks: Scikit-Learn, TensorFlow / Keras, PyTorch, XGBoost.
• Data Visualization Tools: Tableau, Power BI, Matplotlib, Seaborn.
• Big Data Tools: Apache Spark, Databricks.