How to Make Your Data Scientist Resume Stand Out in 2026

Formula: Problems you solve + stack + target role

Data science fresher who builds and evaluates prediction models
for customer churn and demand, using Python, SQL and
scikit-learn, and serves them as simple APIs. Seeking a junior
data scientist role.

Avoid: passionate, AI enthusiast, hard-working (without proof).

SKILLS
Programming:  Python (pandas, NumPy), SQL (CTEs, window functions)
Modelling:    Logistic regression, random forest, gradient boosting,
              time-series baselines
Evaluation:   Cross-validation, precision/recall, ROC-AUC, error analysis
Statistics:   Hypothesis testing, confidence intervals, A/B test basics
Deployment:   FastAPI, Docker, Git, basic CI
Tools:        Jupyter, scikit-learn, Matplotlib

from sklearn.datasets import make_classification
from sklearn.dummy import DummyClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import recall_score, precision_score, accuracy_score

X, y = make_classification(n_samples=4000, weights=[0.9], random_state=11)
X_tr, X_te, y_tr, y_te = train_test_split(X, y, stratify=y, random_state=11)

for name, m in [("majority baseline", DummyClassifier(strategy="most_frequent")),
                ("gradient boosting", GradientBoostingClassifier(random_state=11))]:
    p = m.fit(X_tr, y_tr).predict(X_te)
    print(f"{name:18} acc={accuracy_score(y_te, p):.2f} "
          f"recall={recall_score(y_te, p, zero_division=0):.2f} "
          f"precision={precision_score(y_te, p, zero_division=0):.2f}")
# Majority baseline: high accuracy, zero recall. That is why accuracy alone misleads.

PROJECTS

Subscriber Churn Prediction | Python, SQL, scikit-learn, FastAPI
- Predicted which subscribers might cancel next month using
  usage and billing features from a public telecom dataset
- Optimised recall on the churn class; compared against a
  majority-class and a rule-based baseline on held-out data
- Error analysis showed most misses were short-tenure users;
  added tenure features in response
- Served the model as a FastAPI endpoint in Docker
- github.com/<you>/churn-prediction

# pip install fastapi uvicorn scikit-learn joblib
# Run: uvicorn serve:app --port 8000
import joblib
from fastapi import FastAPI
from pydantic import BaseModel, Field

app = FastAPI()
model = joblib.load("churn_model.joblib")

class Subscriber(BaseModel):
    tenure_months: float = Field(ge=0)
    monthly_charges: float = Field(ge=0)
    support_calls: int = Field(ge=0)

@app.post("/churn-risk")
def churn_risk(s: Subscriber):
    p = model.predict_proba([[s.tenure_months, s.monthly_charges, s.support_calls]])[0][1]
    return {"churn_probability": round(float(p), 3)}

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY serve.py churn_model.joblib ./
RUN useradd --create-home appuser
USER appuser
EXPOSE 8000
CMD ["uvicorn", "serve:app", "--host", "0.0.0.0", "--port", "8000"]

FIRSTNAME LASTNAME
Data Scientist (Fresher) | Python, SQL, scikit-learn
City | email@example.com | +91-XXXXXXXXXX
github.com/<you> | linkedin.com/in/<you> | portfolio-link

SUMMARY
Data science fresher who builds and evaluates prediction models
for churn and demand, and serves them as simple APIs. Seeking a
junior data scientist role.

PROJECTS
(2-3 entries: title | stack, then problem / metric and baseline /
 error analysis / deployment / link)

SKILLS
Programming | Modelling | Evaluation | Statistics | Deployment | Tools

EXPERIENCE / INTERNSHIP (if any)
Role, organisation, dates; 2-3 specific bullets

EDUCATION
Degree, college, year (relevant coursework if useful)

[ ] Summary names problems solved and the target role
[ ] Skills grouped, including evaluation and statistics
[ ] Each project states the metric and baseline
[ ] At least one project is deployed (API, app or batch job)
[ ] No unexplained accuracy claims
[ ] Impact described honestly
[ ] Repos have READMEs; links work
[ ] One page, one column, tailored to the listing