Engineer jobs in India

Data Scientist (IT)Position:

Data ScientistJob type:

Techno-FunctionalPreferred education qualifications:

Bachelor/ Master's degree in Statistics, Operation Research, Computer Science, Data Science OR related quantitative fieldJob location:

IndiaGeography:

SAPMENARequired experience:

6-8 YearsPreferred profile/ skills:



§ 5+ years in developing and implementing forecasting models

§ [Mandatory] Proven track record in data analysis (EDA, profiling, sampling), data engineering (wrangling, storage, pipelines, orchestration)

§ [Mandatory] Proven expertise in time series analysis, regression analysis, and other statistical modelling techniques

§ [Mandatory] Experience in ML algorithms such as ARIMA, Prophet, Random Forests, and Gradient Boosting algorithms (XGBoost, LightGBM, CatBoost)

§ [Mandatory] Experience in model explainability with Shapley plot and data drift detection metrics.

§ [Mandatory] Strong programming & analysis skills with Python and SQL, including experience with relevant forecasting packages

§ [Mandatory] Prior experience on Data Science & ML Engineering on Google Cloud

§ [Mandatory] Proficiency in version control systems such as GitHub

§ [Mandatory] Strong organizational capabilities; and ability to work in a matrix/ multidisciplinary team

§ [Mandatory] Excellent communication and presentation skills, with the ability to explain complex technical concepts to non-technical audience

§ Experience in Beauty or Retail/FMCG industry is preferred

§ Experience in handling large volume of data (>100 GB)

§ Experience in delivering AI-ML projects using Agile methodologies is preferred

§ Proven ability to work proactively and independently to address product requirements and design optimal solutionsJob objectives:

Design, develop, implement, and maintain data science and machine learning solutions to meet enterprise goals. Collaborate with cross-functional teams to leverage statistical modeling, machine learning, and data mining techniques to improve forecast accuracy and aid strategic decision-making across the organization. Scale the proven AI-ML Product across the SAPMENA region.Job description:§ Deep understanding of business/functional needs, problem statements and objectives/success criteria

§ Develop and maintain sophisticated statistical forecasting models, incorporating factors such as seasonality, promotions, media, traffic and other economic indicators

§ Collaborate with internal and external stakeholders including business, data scientists & product team to understand the business and product needs and translate them into actionable data-driven solutions

§ Review MVP implementations, provide recommendations and ensure Data Science best practices and guidelines are followed

§ Evaluate and compare the performance of different forecasting models, recommending optimal approaches for various business scenarios

§ Analyze large and complex datasets to identify patterns, insights, and potential risks and opportunities

§ Communicate forecasting results and insights to both technical and non-technical audiences through clear visualizations and presentations

§ Stay up to date with the latest advancements in forecasting techniques and technologies, continuously seeking opportunities for improvement

§ Contribute to the development of a robust data infrastructure for AI-ML solutions, ensuring data quality and accessibility

§ Collaborate with other data scientists and engineers to build and deploy scalable AI-ML solutions

Job RequirementsEducation

* Bachelor's degree required, preferably with a quantitative focus (Statistics, Business Analytics, Data Science, Math, Economics, etc.)

* Master's degree preferred (MBA/MS Computer Science/M.Tech Computer Science, etc.)Relevant Experience

* 5 - 7 years for Data Scientist

* Relevant working experience in a data science/advanced analytics roleBehavioural Skills

* Delivery Excellence

* Business disposition

* Social intelligence

* Innovation and agilityExperience

* Functional Analytics (Supply chain analytics, Marketing Analytics, Customer Analytics, etc.)

* Statistical modelling using Analytical tools (R, Python, KNIME, etc.)

* Knowledge of statistics and experimental design (A/B testing, hypothesis testing, causal inference)

* Practical experience building scalable ML models, feature engineering, model evaluation metrics, and statistical inference.

* Practical experience deploying models using MLOps tools and practices (e.g., MLflow, DVC, Docker, etc.)

* Strong coding proficiency in Python (Pandas, Scikit-learn, PyTorch/TensorFlow, etc.)

* Big data technologies & framework (AWS, Azure, GCP, Hadoop, Spark, etc.)

* Enterprise reporting systems, relational (MySQL, Microsoft SQL Server etc.), non-relational (MongoDB, DynamoDB) database management systems and Data Engineering tools

* Business intelligence & reporting (Power BI, Tableau, Alteryx, etc.)

* Microsoft Office applications (MS Excel, etc.)Roles & Responsibilities

Analytics & Strategy

1. Analyse large-scale structured and unstructured data; develop deep-dive analyses and machine learning models in retail, marketing, merchandising, and other areas of the business

2. Utilize data mining, statistical and machine learning techniques to derive business value from store, product, operations, financial, and customer transactional data

3. Apply multiple algorithms or architectures and recommend the best model with in-depth description to evangelize data-driven business decisions

4. Utilize cloud setup to extract processed data for statistical modelling and big data analysis, and visualization tools to represent large sets of time series/cross-sectional dataOperational Excellence

1. Follow industry standards in coding solutions and follow programming life cycle to ensure standard practices across the project

2. Structure hypothesis, build thoughtful analyses, develop underlying data models and bring clarity to previously undefined problems

3. Partner with Data Engineering to build, design and maintain core data infrastructure, pipelines and data workflows to automate dashboards and analysesStakeholder Engagement

1. Working collaboratively across multiple sets of stakeholders - Business functions, Data Engineers, Data Visualization experts to deliver on project deliverables

2. Articulate complex data science models to business teams and present the insights in easily understandable and innovative formats