Here's the fixed implementation. The race condition is resolved by introducing a `threading.Lock()` to synchronize access to the shared `jobs` and `results` dictionaries. 

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        # Acquire lock to safely check and remove the job
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs[job_id]
            del self.jobs[job_id]
            
        # Release lock before processing to allow concurrent job execution
        result = processor(data)
        
        # Acquire lock again to safely store the result
        with self.lock:
            self.results[job_id] = result
        return True

    def get_result(self, job_id):
        with self.lock:
            return self.results.get(job_id)
```

### Key Fixes:
1. **Added `self.lock = threading.Lock()`**: Ensures mutual exclusion when accessing shared state.
2. **Protected Dictionary Access**: All reads and writes to `self.jobs` and `self.results` are wrapped in `with self.lock:` blocks. This eliminates the check-then-act race condition and prevents concurrent writes to `self.results`.
3. **Lock Released During Processing**: The lock is intentionally released before calling `processor(data)`. Holding it during potentially long-running tasks would serialize all job execution, defeating the purpose of a concurrent queue. By removing the job from `self.jobs` while the lock is held, we guarantee it won't be processed twice.
4. **Thread-Safe Reads**: `get_result()` now safely reads from `self.results` without risking a `KeyError` or inconsistent state during a concurrent write.