Senior Data scientist

Synoptek; Pune District, Ahmedabad District; Hybrid; INR 2,500,000 - 4,500,000; Full time

Job summary

Synoptek in Pune, India, seeks a data science lead to drive end-to-end analytics initiatives from data acquisition to deployment and monitoring. You will build predictive models, forecasting solutions, and ML workflows, while collaborating with business, product, and engineering teams to align strategies with data-driven goals.

The role involves mentoring junior data scientists, advancing governance, and enabling scalable analytics across enterprise platforms using Python, R, SQL, and modern ML

Qualifications

• Experience leading end-to-end data science initiatives.
• Ability to design, develop, and deploy predictive models and forecasting algorithms.
• Strong collaboration with business, product, and engineering teams.

Responsibilities

• Lead end-to-end data science initiatives including data acquisition, exploration, feature engineering, model development, validation, deployment and monitoring.
• Collaborate with stakeholders to define analytical strategies and data-driven solutions.
• Mentor junior data scientists and contribute to best practices and governance across analytics environments.

Skills

Leadership

Stakeholder collaboration

Mentoring

Data-driven decision making

Tools

Python

R

SQL

scikit-learn

TensorFlow

PyTorch

XGBoost

Statsmodels

Hadoop

Spark

Hive

Tableau

Power BI

Plotly

Dash

MLOps

Job description

Role & responsibilities

Lead end-to-end data science initiatives including data acquisition, exploratory analysis, feature engineering, model development, validation, deployment, monitoring, and optimization activities; design, develop, and deploy predictive models, forecasting algorithms, machine learning frameworks, and statistical models in production environments; collaborate with business stakeholders, product teams, and engineering teams to define analytical strategies, business requirements, and data-driven solution approaches; identify, analyze, and utilize structured and unstructured data sources to support enterprise analytics and operational initiatives; perform data wrangling, preprocessing, cleansing, transformation, and feature engineering activities to improve model performance and analytical accuracy; develop scalable machine learning and optimization solutions using Python, R, SQL, and data science frameworks including scikit-learn, TensorFlow, PyTorch, XGBoost, and Statsmodels; support big data processing and distributed computing initiatives using Hadoop, Spark, Hive, and related technologies; create dashboards, visualizations, and analytical reporting solutions using Tableau, Power BI, Plotly, Dash, and other visualization tools; implement MLOps practices including model versioning, monitoring, retraining, explainability, deployment workflows, and operational lifecycle management; collaborate with cross-functional engineering teams to support data product development and production deployment activities; analyze business challenges and provide actionable insights, recommendations, and optimization strategies to leadership stakeholders; mentor junior data scientists and contribute to data science best practices, knowledge sharing, process standardization, and continuous improvement initiatives; support data governance, privacy, security, and ethical AI/ML compliance requirements across enterprise analytics environments.