TasksTasks

Strong knowledge of Applied AI ML & Deep Learning Data Science techniques, Hardcore in ANN /Deep Learning /Machine Learning/NLP
Deep knowledge about machine learning algorithms such as tree-based methods, clustering, regression and classification, dimension reduction techniques, linear regression, Logistic regression, k-means, time series forecasting, Hypothesis testing (ANOVA, t-test, etc.), random forest, SVMs, Naive Bayes, gradient boosting, kNN, Deep learning algorithms like CNN, ANN and Reinforcement learning, Anomaly detection.
In-depth understanding of Statistical concepts e.g. Probability distributions, statistical tests, correlation analysis, descriptive statistics, kernels, ROC, F1-Score etc.
Advanced coding experience in at least one programming language (Python, Pyspark) & Strong experience in object-oriented concepts.
Good to have advanced experience in one or more of the following: Spark, Databricks, Azure technical stack
Good to have experience in model deployment to cloud/on-prem.
Good Communication & presentation skills.

We are looking for a Data Scientist to lead data-driven solutions across our business, from data preparation through model deployment and monitoring. This role involves transforming complex business questions into actionable insights and predictive models.

Key Responsibilities:

Problem Definition: Partner with stakeholders to translate business goals into clear, data-focused questions and define project scope and success metrics.
Data Collection and Preparation: Gather, clean, and preprocess data from diverse sources, ensuring quality and consistency, and engineer features to enhance model performance.
Exploratory Data Analysis (EDA): Use statistical methods and visualization to uncover trends and validate assumptions, summarizing key insights for business alignment.
Model Building and Evaluation: Select, train, and refine models suited to the business problem, evaluating performance with relevant metrics and documenting model assumptions.
Deployment and Monitoring: Work with engineering teams to deploy models, establish performance monitoring, retrain as necessary, and incorporate feedback to improve accuracy.

Strong knowledge of Applied AI ML & Deep Learning Data Science techniques, Hardcore in ANN /Deep Learning /Machine Learning/NLP
Deep knowledge about machine learning algorithms such as tree-based methods, clustering, regression and classification, dimension reduction techniques, linear regression, Logistic regression, k-means, time series forecasting, Hypothesis testing (ANOVA, t-test, etc.), random forest, SVMs, Naive Bayes, gradient boosting, kNN, Deep learning algorithms like CNN, ANN and Reinforcement learning, Anomaly detection.
In-depth understanding of Statistical concepts e.g. Probability distributions, statistical tests, correlation analysis, descriptive statistics, kernels, ROC, F1-Score etc.
Advanced coding experience in at least one programming language (Python, Pyspark) & Strong experience in object-oriented concepts.
Good to have advanced experience in one or more of the following: Spark, Databricks, Azure technical stack
Good to have experience in model deployment to cloud/on-prem.
Good Communication & presentation skills.

Key Responsibilities:

Problem Definition: Partner with stakeholders to translate business goals into clear, data-focused questions and define project scope and success metrics.
Data Collection and Preparation: Gather, clean, and preprocess data from diverse sources, ensuring quality and consistency, and engineer features to enhance model performance.
Exploratory Data Analysis (EDA): Use statistical methods and visualization to uncover trends and validate assumptions, summarizing key insights for business alignment.
Model Building and Evaluation: Select, train, and refine models suited to the business problem, evaluating performance with relevant metrics and documenting model assumptions.
Deployment and Monitoring: Work with engineering teams to deploy models, establish performance monitoring, retrain as necessary, and incorporate feedback to improve accuracy.

QualificationsQualifications

Required Qualifications

Bachelor’s/Master’s in Data Science, Statistics, Computer Science, or related field.
Experience in end to end model development to deployment and performance monitoring.
Strong communication skills to convey insights to technical and non-technical audiences.
Minimum of 4-7 years of relevant experience with focus on advanced analytics and big data
Ability to assess the current situation on Data and Analytics Landscape, then devise strategy for the domain
Good at managing different stakeholders and experience in managing multiple projects at a time
Should have ability to transform a business problem to data science problem
Have the ability to discover new opportunities where analytical techniques can be leveraged for solving business problems.

Our company cultureOur company culture

At Daimler Truck, we promote diversity and foster an inclusive corporate culture. We value the individual strengths of our employees, as these lead to the best team performance and thus to the success of our company. Inclusion and Equal opportunities are important to us – regardless of where you come from and who you are. We look forward to receiving applications from people of all cultures and genders, parents, people with disabilities and people from the LGBTIQ+ community.