About the role
Key Responsibilities:
- Analyze and interpret large, complex datasets using statistical and machine learning techniques.
- Develop predictive models and machine learning solutions using frameworks like scikit-learn, TensorFlow, or PyTorch .
- Perform statistical analysis including hypothesis testing, regression modeling , and A/B testing .
- Clean, manipulate, and prepare data using Python libraries such as Pandas, NumPy .
- Visualize insights and trends using Matplotlib, Seaborn , or ggplot2 to support storytelling and decision-making.
- Write efficient queries using SQL to extract and manipulate data from relational databases.
- Collaborate with business stakeholders to understand requirements, identify data-driven opportunities, and present findings.
- Work with big data tools (e.g., Spark, Hive, Hadoop ) to manage and process large-scale datasets.
- Deploy models and analytics pipelines to the cloud (AWS, Azure, GCP) and support their ongoing performance.
- Use version control (Git) and documentation best practices to ensure reproducibility and collaboration.
Required Qualifications:
- Proficiency in Python for data analysis, modeling, and scripting.
- Strong knowledge of statistics and probability , including experience with hypothesis testing, regression, and inferential statistics.
- Hands-on experience with machine learning algorithms (classification, regression, clustering, ensemble methods).
- Experience with data science tools such as Pandas, NumPy, Scikit-learn .
- Strong data visualization skills using tools like Matplotlib, Seaborn , or ggplot2 .
- Proficient in SQL and understanding of relational databases.
- Familiarity with big data frameworks (Spark, Hive, or Hadoop).
- Experience working on cloud platforms such as AWS, Azure, or GCP.
- Understanding of data preprocessing techniques (cleaning, feature engineering, dimensionality reduction).
- Experience with version control systems like Git.
Preferred Qualifications:
- Master’s or PhD in Data Science, Statistics, Computer Science, Mathematics , or a related field.
- Experience with deep learning and neural networks .
- Exposure to MLOps , model deployment, and monitoring tools.
- Domain knowledge in [Finance / Healthcare / E-commerce / etc.] (customize as per your industry).
About Galent
Galent is an AI-native digital engineering firm at the forefront of the AI revolution, dedicated to delivering unified, enterprise-ready AI solutions that transform businesses and industries. Our mission is to empower organizations to thrive in an ever-evolving digital landscape through cutting-edge AI-native services, consulting, and digital engineering.
As enterprises face the complexities of integrating advanced technologies, Galent provides scalable, AI-enabled solutions that optimize processes, enhance productivity, and unlock new growth opportunities.
Our proprietary AI platforms along with our niche expertise, enable us to solve complex challenges across industries. Galent’s approach combines the latest advancements in AI with deep market insights to drive meaningful outcomes for our clients.
By enabling composable businesses that adapt to change, Galent empowers organizations to harness the full potential of AI and become leaders in their fields.
About the role
Key Responsibilities:
- Analyze and interpret large, complex datasets using statistical and machine learning techniques.
- Develop predictive models and machine learning solutions using frameworks like scikit-learn, TensorFlow, or PyTorch .
- Perform statistical analysis including hypothesis testing, regression modeling , and A/B testing .
- Clean, manipulate, and prepare data using Python libraries such as Pandas, NumPy .
- Visualize insights and trends using Matplotlib, Seaborn , or ggplot2 to support storytelling and decision-making.
- Write efficient queries using SQL to extract and manipulate data from relational databases.
- Collaborate with business stakeholders to understand requirements, identify data-driven opportunities, and present findings.
- Work with big data tools (e.g., Spark, Hive, Hadoop ) to manage and process large-scale datasets.
- Deploy models and analytics pipelines to the cloud (AWS, Azure, GCP) and support their ongoing performance.
- Use version control (Git) and documentation best practices to ensure reproducibility and collaboration.
Required Qualifications:
- Proficiency in Python for data analysis, modeling, and scripting.
- Strong knowledge of statistics and probability , including experience with hypothesis testing, regression, and inferential statistics.
- Hands-on experience with machine learning algorithms (classification, regression, clustering, ensemble methods).
- Experience with data science tools such as Pandas, NumPy, Scikit-learn .
- Strong data visualization skills using tools like Matplotlib, Seaborn , or ggplot2 .
- Proficient in SQL and understanding of relational databases.
- Familiarity with big data frameworks (Spark, Hive, or Hadoop).
- Experience working on cloud platforms such as AWS, Azure, or GCP.
- Understanding of data preprocessing techniques (cleaning, feature engineering, dimensionality reduction).
- Experience with version control systems like Git.
Preferred Qualifications:
- Master’s or PhD in Data Science, Statistics, Computer Science, Mathematics , or a related field.
- Experience with deep learning and neural networks .
- Exposure to MLOps , model deployment, and monitoring tools.
- Domain knowledge in [Finance / Healthcare / E-commerce / etc.] (customize as per your industry).
About Galent
Galent is an AI-native digital engineering firm at the forefront of the AI revolution, dedicated to delivering unified, enterprise-ready AI solutions that transform businesses and industries. Our mission is to empower organizations to thrive in an ever-evolving digital landscape through cutting-edge AI-native services, consulting, and digital engineering.
As enterprises face the complexities of integrating advanced technologies, Galent provides scalable, AI-enabled solutions that optimize processes, enhance productivity, and unlock new growth opportunities.
Our proprietary AI platforms along with our niche expertise, enable us to solve complex challenges across industries. Galent’s approach combines the latest advancements in AI with deep market insights to drive meaningful outcomes for our clients.
By enabling composable businesses that adapt to change, Galent empowers organizations to harness the full potential of AI and become leaders in their fields.